{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inferelator BONOBOs analysis of GCN4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load modules\n",
    "import pandas as pd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import glob\n",
    "import gseapy as gp\n",
    "from gseapy import Biomart\n",
    "# yeast\n",
    "yeast = gp.get_library_name(organism='Yeast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_simple_yeast_names(ids):\n",
    "    bm = Biomart()\n",
    "    queries ={'ensembl_gene_id': ids } # need to be a dict object\n",
    "    results = bm.query(dataset='scerevisiae_gene_ensembl', filters = queries, attributes=['ensembl_gene_id', 'external_gene_name'])\n",
    "    gene_list_gene_name = results.dropna()['external_gene_name'].tolist()\n",
    "    return(gene_list_gene_name)\n",
    "\n",
    "def get_simple_yeast_correspondence(ids):\n",
    "    bm = Biomart()\n",
    "    queries ={'ensembl_gene_id': ids } # need to be a dict object\n",
    "    results = bm.query(dataset='scerevisiae_gene_ensembl', filters = queries, attributes=['ensembl_gene_id', 'external_gene_name'])\n",
    "    return(results)\n",
    "\n",
    "def get_gene_yeast_gene_name(gl, ngenes = 100):\n",
    "    bm = Biomart()\n",
    "    gene_list_gene_name = []\n",
    "    if len(gl)>ngenes:\n",
    "        print('WARNING: there are more than %d genes (%d tot)' %(ngenes, len(gl)))\n",
    "        for k,i in enumerate(range(len(gl)//ngenes)):\n",
    "            if k%5==0:\n",
    "                print('Iteration %d' %k)\n",
    "            gl_temp = gl[k*ngenes:(k+1)*ngenes]\n",
    "            glgn_temp = get_simple_yeast_names(gl_temp)\n",
    "            gene_list_gene_name = gene_list_gene_name + glgn_temp\n",
    "    else:\n",
    "        gene_list_gene_name = get_simple_yeast_names(gl)\n",
    "        \n",
    "    return(gene_list_gene_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WT(ho)_AmmoniumSulfate</th>\n",
       "      <th>WT(ho)_CStarve</th>\n",
       "      <th>WT(ho)_Glutamine</th>\n",
       "      <th>WT(ho)_MinimalEtOH</th>\n",
       "      <th>WT(ho)_MinimalGlucose</th>\n",
       "      <th>WT(ho)_Proline</th>\n",
       "      <th>WT(ho)_Urea</th>\n",
       "      <th>WT(ho)_YPD</th>\n",
       "      <th>WT(ho)_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>...</th>\n",
       "      <th>stp2_CStarve</th>\n",
       "      <th>stp2_Glutamine</th>\n",
       "      <th>stp2_MinimalEtOH</th>\n",
       "      <th>stp2_MinimalGlucose</th>\n",
       "      <th>stp2_Proline</th>\n",
       "      <th>stp2_Urea</th>\n",
       "      <th>stp2_YPD</th>\n",
       "      <th>stp2_YPDDiauxic</th>\n",
       "      <th>stp2_YPDRapa</th>\n",
       "      <th>stp2_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YDL248W</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004040</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL247W</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL246C</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL245C</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL244W</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012474</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006780</td>\n",
       "      <td>0.001024</td>\n",
       "      <td>0.028259</td>\n",
       "      <td>0.022282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035091</td>\n",
       "      <td>0.012073</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.012945</td>\n",
       "      <td>0.024693</td>\n",
       "      <td>0.061875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q0255</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q0275</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPM1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KANMX</th>\n",
       "      <td>2.360386</td>\n",
       "      <td>1.014260</td>\n",
       "      <td>2.011293</td>\n",
       "      <td>1.759183</td>\n",
       "      <td>2.200487</td>\n",
       "      <td>1.268030</td>\n",
       "      <td>2.427748</td>\n",
       "      <td>1.901312</td>\n",
       "      <td>1.486226</td>\n",
       "      <td>1.456467</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001357</td>\n",
       "      <td>2.033365</td>\n",
       "      <td>2.207096</td>\n",
       "      <td>2.372927</td>\n",
       "      <td>1.500772</td>\n",
       "      <td>2.615808</td>\n",
       "      <td>1.996838</td>\n",
       "      <td>1.457781</td>\n",
       "      <td>1.563132</td>\n",
       "      <td>2.406881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NATMX</th>\n",
       "      <td>2.588820</td>\n",
       "      <td>1.358721</td>\n",
       "      <td>2.786845</td>\n",
       "      <td>1.978345</td>\n",
       "      <td>2.986127</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>2.659736</td>\n",
       "      <td>3.098374</td>\n",
       "      <td>1.951807</td>\n",
       "      <td>2.841197</td>\n",
       "      <td>...</td>\n",
       "      <td>1.343930</td>\n",
       "      <td>2.772589</td>\n",
       "      <td>2.268068</td>\n",
       "      <td>3.187456</td>\n",
       "      <td>1.753067</td>\n",
       "      <td>2.841871</td>\n",
       "      <td>3.260749</td>\n",
       "      <td>2.014251</td>\n",
       "      <td>3.067180</td>\n",
       "      <td>3.015396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6520 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         WT(ho)_AmmoniumSulfate  WT(ho)_CStarve  WT(ho)_Glutamine  \\\n",
       "YDL248W                0.000000        0.000000          0.000000   \n",
       "YDL247W                0.000000        0.000000          0.000000   \n",
       "YDL246C                0.000000        0.000000          0.000000   \n",
       "YDL245C                0.000000        0.004175          0.000000   \n",
       "YDL244W                0.000000        0.012474          0.006734   \n",
       "...                         ...             ...               ...   \n",
       "Q0255                  0.000000        0.000000          0.000000   \n",
       "Q0275                  0.000000        0.000000          0.000000   \n",
       "RPM1                   0.000000        0.000000          0.000000   \n",
       "KANMX                  2.360386        1.014260          2.011293   \n",
       "NATMX                  2.588820        1.358721          2.786845   \n",
       "\n",
       "         WT(ho)_MinimalEtOH  WT(ho)_MinimalGlucose  WT(ho)_Proline  \\\n",
       "YDL248W            0.000000               0.004890        0.015267   \n",
       "YDL247W            0.000000               0.000000        0.000000   \n",
       "YDL246C            0.000000               0.000000        0.000000   \n",
       "YDL245C            0.000000               0.000000        0.000000   \n",
       "YDL244W            0.000000               0.000000        0.000000   \n",
       "...                     ...                    ...             ...   \n",
       "Q0255              0.000000               0.000000        0.000000   \n",
       "Q0275              0.000000               0.004890        0.000000   \n",
       "RPM1               0.000000               0.000000        0.000000   \n",
       "KANMX              1.759183               2.200487        1.268030   \n",
       "NATMX              1.978345               2.986127        1.757858   \n",
       "\n",
       "         WT(ho)_Urea  WT(ho)_YPD  WT(ho)_YPDDiauxic  WT(ho)_YPDRapa  ...  \\\n",
       "YDL248W     0.000000    0.000000           0.003180        0.002144  ...   \n",
       "YDL247W     0.000000    0.000000           0.000000        0.000000  ...   \n",
       "YDL246C     0.000000    0.000000           0.000000        0.000000  ...   \n",
       "YDL245C     0.000000    0.000000           0.000000        0.002144  ...   \n",
       "YDL244W     0.006780    0.001024           0.028259        0.022282  ...   \n",
       "...              ...         ...                ...             ...  ...   \n",
       "Q0255       0.000000    0.000000           0.000000        0.000000  ...   \n",
       "Q0275       0.000000    0.000000           0.003180        0.000000  ...   \n",
       "RPM1        0.000000    0.000000           0.000000        0.000000  ...   \n",
       "KANMX       2.427748    1.901312           1.486226        1.456467  ...   \n",
       "NATMX       2.659736    3.098374           1.951807        2.841197  ...   \n",
       "\n",
       "         stp2_CStarve  stp2_Glutamine  stp2_MinimalEtOH  stp2_MinimalGlucose  \\\n",
       "YDL248W      0.000000        0.000000          0.000000             0.004040   \n",
       "YDL247W      0.000000        0.000000          0.000000             0.000000   \n",
       "YDL246C      0.000000        0.000000          0.000000             0.000000   \n",
       "YDL245C      0.000000        0.000000          0.017700             0.000000   \n",
       "YDL244W      0.017778        0.000000          0.035091             0.012073   \n",
       "...               ...             ...               ...                  ...   \n",
       "Q0255        0.000000        0.000000          0.000000             0.000000   \n",
       "Q0275        0.000000        0.000000          0.000000             0.000000   \n",
       "RPM1         0.000000        0.000000          0.000000             0.000000   \n",
       "KANMX        1.001357        2.033365          2.207096             2.372927   \n",
       "NATMX        1.343930        2.772589          2.268068             3.187456   \n",
       "\n",
       "         stp2_Proline  stp2_Urea  stp2_YPD  stp2_YPDDiauxic  stp2_YPDRapa  \\\n",
       "YDL248W      0.009852   0.000000  0.000000         0.006494      0.003743   \n",
       "YDL247W      0.000000   0.000000  0.000000         0.000000      0.000000   \n",
       "YDL246C      0.000000   0.000000  0.000000         0.000000      0.000000   \n",
       "YDL245C      0.000000   0.000000  0.000000         0.000000      0.001249   \n",
       "YDL244W      0.009852   0.000000  0.001278         0.012945      0.024693   \n",
       "...               ...        ...       ...              ...           ...   \n",
       "Q0255        0.000000   0.000000  0.000000         0.000000      0.000000   \n",
       "Q0275        0.000000   0.000000  0.000000         0.006494      0.001249   \n",
       "RPM1         0.000000   0.000000  0.000000         0.000000      0.000000   \n",
       "KANMX        1.500772   2.615808  1.996838         1.457781      1.563132   \n",
       "NATMX        1.753067   2.841871  3.260749         2.014251      3.067180   \n",
       "\n",
       "         stp2_YPEtOH  \n",
       "YDL248W     0.000000  \n",
       "YDL247W     0.000000  \n",
       "YDL246C     0.000000  \n",
       "YDL245C     0.000000  \n",
       "YDL244W     0.061875  \n",
       "...              ...  \n",
       "Q0255       0.000000  \n",
       "Q0275       0.000000  \n",
       "RPM1        0.000000  \n",
       "KANMX       2.406881  \n",
       "NATMX       3.015396  \n",
       "\n",
       "[6520 rows x 132 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaned and pseudobulked data\n",
    "params = {\n",
    "    'nonzero' : {'yeast_expression' : '../data/processed/pseudobulk_nonzero_logcounts.tsv',\\\n",
    "               'output_folder' : '../results/new_pseudobulk_nonzero/'},\\\n",
    "    'perc02' : {'yeast_expression': '../data/processed/pseudobulk_perc02_logcounts.tsv',\\\n",
    "              'output_folder' : '../results/new_pseudobulk_perc02/'}\n",
    "}\n",
    "params\n",
    "choice = 'nonzero'\n",
    "\n",
    "import os\n",
    "yeast_expression = params[choice]['yeast_expression']\n",
    "output_folder = params[choice]['output_folder']\n",
    "\n",
    "results_folder = output_folder + 'gcn4/'\n",
    "os.mkdir(results_folder)\n",
    "\n",
    "expr = pd.read_csv(yeast_expression, sep = '\\t', index_col = 0)\n",
    "expr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal82_MinimalGlucose</th>\n",
       "      <th>dal81_YPDDiauxic</th>\n",
       "      <th>gln3_YPD</th>\n",
       "      <th>stp1_YPDRapa</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal82_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>gat1_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>rtg1_CStarve</th>\n",
       "      <th>rtg3_YPDRapa</th>\n",
       "      <th>gln3_YPDRapa</th>\n",
       "      <th>rtg3_Urea</th>\n",
       "      <th>gcn4_YPDDiauxic</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>rtg1_Glutamine</th>\n",
       "      <th>rtg1_MinimalGlucose</th>\n",
       "      <th>dal82_YPD</th>\n",
       "      <th>edge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NME1</td>\n",
       "      <td>HRA1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NME1-HRA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PWR1</td>\n",
       "      <td>YFL012W</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.887625</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>...</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.919331</td>\n",
       "      <td>PWR1-YFL012W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PWR1</td>\n",
       "      <td>YFR035C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PWR1-YFR035C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q0045</td>\n",
       "      <td>Q0050</td>\n",
       "      <td>0.842767</td>\n",
       "      <td>0.894936</td>\n",
       "      <td>0.863078</td>\n",
       "      <td>0.861894</td>\n",
       "      <td>0.860010</td>\n",
       "      <td>0.870263</td>\n",
       "      <td>0.861966</td>\n",
       "      <td>0.863271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861807</td>\n",
       "      <td>0.862441</td>\n",
       "      <td>0.860253</td>\n",
       "      <td>0.858888</td>\n",
       "      <td>0.872088</td>\n",
       "      <td>0.864021</td>\n",
       "      <td>0.860100</td>\n",
       "      <td>0.843846</td>\n",
       "      <td>0.863212</td>\n",
       "      <td>Q0045-Q0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q0045</td>\n",
       "      <td>Q0075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.793110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.806043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.811269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q0045-Q0075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gene1    gene2  dal82_MinimalGlucose  dal81_YPDDiauxic  gln3_YPD  \\\n",
       "0   NME1     HRA1                   NaN               NaN       NaN   \n",
       "1   PWR1  YFL012W              0.921189          0.921188  0.887625   \n",
       "2   PWR1  YFR035C                   NaN               NaN       NaN   \n",
       "3  Q0045    Q0050              0.842767          0.894936  0.863078   \n",
       "4  Q0045    Q0075                   NaN          0.793110       NaN   \n",
       "\n",
       "   stp1_YPDRapa  dal81_MinimalEtOH  dal82_YPDDiauxic  WT(ho)_YPDRapa  \\\n",
       "0           NaN                NaN               NaN             NaN   \n",
       "1      0.921189           0.921189          0.921188        0.921189   \n",
       "2           NaN                NaN               NaN             NaN   \n",
       "3      0.861894           0.860010          0.870263        0.861966   \n",
       "4           NaN                NaN          0.806043             NaN   \n",
       "\n",
       "   gat1_YPD  ...  rtg1_CStarve  rtg3_YPDRapa  gln3_YPDRapa  rtg3_Urea  \\\n",
       "0       NaN  ...           NaN           NaN           NaN        NaN   \n",
       "1  0.921189  ...      0.921189      0.921189      0.921189   0.921189   \n",
       "2       NaN  ...           NaN           NaN           NaN        NaN   \n",
       "3  0.863271  ...      0.861807      0.862441      0.860253   0.858888   \n",
       "4       NaN  ...           NaN           NaN           NaN        NaN   \n",
       "\n",
       "   gcn4_YPDDiauxic  dal81_Urea  rtg1_Glutamine  rtg1_MinimalGlucose  \\\n",
       "0              NaN         NaN             NaN                  NaN   \n",
       "1         0.921188    0.921189        0.921189             0.921189   \n",
       "2              NaN         NaN             NaN                  NaN   \n",
       "3         0.872088    0.864021        0.860100             0.843846   \n",
       "4         0.811269         NaN             NaN                  NaN   \n",
       "\n",
       "   dal82_YPD          edge  \n",
       "0        NaN     NME1-HRA1  \n",
       "1   0.919331  PWR1-YFL012W  \n",
       "2        NaN  PWR1-YFR035C  \n",
       "3   0.863212   Q0045-Q0050  \n",
       "4        NaN   Q0045-Q0075  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_hdf(output_folder+'bonobo_sparse_001.h5')\n",
    "df['edge'] = df['gene1'] + '-' + df['gene2']\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_old = pd.read_csv('../../bonobo_paper_yeast/data/networks/bonobo_inferelator_sparse_20230908/bonobo_all_outer.tsv', index_col=0, sep='\\t')\n",
    "#df_old['edge'] = df_old['gene1'] + '-' + df_old['gene2']\n",
    "#df_old\n",
    "\n",
    "#df005 = pd.read_hdf('../results/pseudobulk_nonzero/bonobo_sparse_005.h5')\n",
    "#df005['edge'] = df005['gene1'] + '-' + df005['gene2']\n",
    "#df005.shape\n",
    "#df005.head()\n",
    "\n",
    "df01 = pd.read_hdf('../results/pseudobulk_nonzero/bonobo_sparse_01.h5')\n",
    "#df01['edge'] = df01['gene1'] + '-' + df01['gene2']\n",
    "#df01.shape\n",
    "#df01.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "bonobo_names_df = pd.DataFrame()\n",
    "bonobo_names_df.index = df.columns[2:-1]\n",
    "bonobo_names_df['genotype'] = [i.split('_')[0] for i in bonobo_names_df.index]\n",
    "bonobo_names_df['medium'] = [i.split('_')[1] for i in bonobo_names_df.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal82_MinimalGlucose</th>\n",
       "      <th>dal81_YPDDiauxic</th>\n",
       "      <th>gln3_YPD</th>\n",
       "      <th>stp1_YPDRapa</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal82_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>gat1_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>rtg1_CStarve</th>\n",
       "      <th>rtg3_YPDRapa</th>\n",
       "      <th>gln3_YPDRapa</th>\n",
       "      <th>rtg3_Urea</th>\n",
       "      <th>gcn4_YPDDiauxic</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>rtg1_Glutamine</th>\n",
       "      <th>rtg1_MinimalGlucose</th>\n",
       "      <th>dal82_YPD</th>\n",
       "      <th>edge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q0045</td>\n",
       "      <td>Q0050</td>\n",
       "      <td>0.842914</td>\n",
       "      <td>0.894710</td>\n",
       "      <td>0.863053</td>\n",
       "      <td>0.861879</td>\n",
       "      <td>0.860011</td>\n",
       "      <td>0.870177</td>\n",
       "      <td>0.861951</td>\n",
       "      <td>0.863244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861793</td>\n",
       "      <td>0.862422</td>\n",
       "      <td>0.860251</td>\n",
       "      <td>0.858898</td>\n",
       "      <td>0.871985</td>\n",
       "      <td>0.863989</td>\n",
       "      <td>0.860099</td>\n",
       "      <td>0.843983</td>\n",
       "      <td>0.863186</td>\n",
       "      <td>Q0045-Q0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q0045</td>\n",
       "      <td>Q0075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.793003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.805861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.787027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.786055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.811052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.786862</td>\n",
       "      <td>Q0045-Q0075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q0045</td>\n",
       "      <td>Q0130</td>\n",
       "      <td>0.889348</td>\n",
       "      <td>0.911355</td>\n",
       "      <td>0.891856</td>\n",
       "      <td>0.891194</td>\n",
       "      <td>0.882906</td>\n",
       "      <td>0.894871</td>\n",
       "      <td>0.891393</td>\n",
       "      <td>0.892049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.891529</td>\n",
       "      <td>0.891388</td>\n",
       "      <td>0.891234</td>\n",
       "      <td>0.889926</td>\n",
       "      <td>0.900743</td>\n",
       "      <td>0.889429</td>\n",
       "      <td>0.888395</td>\n",
       "      <td>0.887932</td>\n",
       "      <td>0.892009</td>\n",
       "      <td>Q0045-Q0130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q0045</td>\n",
       "      <td>X21S_rRNA</td>\n",
       "      <td>0.866942</td>\n",
       "      <td>0.887128</td>\n",
       "      <td>0.871789</td>\n",
       "      <td>0.871153</td>\n",
       "      <td>0.850336</td>\n",
       "      <td>0.882174</td>\n",
       "      <td>0.871102</td>\n",
       "      <td>0.872061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.871204</td>\n",
       "      <td>0.871253</td>\n",
       "      <td>0.871217</td>\n",
       "      <td>0.869499</td>\n",
       "      <td>0.885671</td>\n",
       "      <td>0.872017</td>\n",
       "      <td>0.866813</td>\n",
       "      <td>0.855822</td>\n",
       "      <td>0.871797</td>\n",
       "      <td>Q0045-X21S_rRNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q0050</td>\n",
       "      <td>Q0075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.808986</td>\n",
       "      <td>0.802799</td>\n",
       "      <td>0.801896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.831787</td>\n",
       "      <td>0.801983</td>\n",
       "      <td>0.803651</td>\n",
       "      <td>...</td>\n",
       "      <td>0.801930</td>\n",
       "      <td>0.802308</td>\n",
       "      <td>0.800460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.832947</td>\n",
       "      <td>0.804640</td>\n",
       "      <td>0.803859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.803727</td>\n",
       "      <td>Q0050-Q0075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gene1      gene2  dal82_MinimalGlucose  dal81_YPDDiauxic  gln3_YPD  \\\n",
       "0  Q0045      Q0050              0.842914          0.894710  0.863053   \n",
       "1  Q0045      Q0075                   NaN          0.793003       NaN   \n",
       "2  Q0045      Q0130              0.889348          0.911355  0.891856   \n",
       "3  Q0045  X21S_rRNA              0.866942          0.887128  0.871789   \n",
       "4  Q0050      Q0075                   NaN          0.808986  0.802799   \n",
       "\n",
       "   stp1_YPDRapa  dal81_MinimalEtOH  dal82_YPDDiauxic  WT(ho)_YPDRapa  \\\n",
       "0      0.861879           0.860011          0.870177        0.861951   \n",
       "1           NaN                NaN          0.805861             NaN   \n",
       "2      0.891194           0.882906          0.894871        0.891393   \n",
       "3      0.871153           0.850336          0.882174        0.871102   \n",
       "4      0.801896                NaN          0.831787        0.801983   \n",
       "\n",
       "   gat1_YPD  ...  rtg1_CStarve  rtg3_YPDRapa  gln3_YPDRapa  rtg3_Urea  \\\n",
       "0  0.863244  ...      0.861793      0.862422      0.860251   0.858898   \n",
       "1  0.787027  ...      0.786055           NaN           NaN        NaN   \n",
       "2  0.892049  ...      0.891529      0.891388      0.891234   0.889926   \n",
       "3  0.872061  ...      0.871204      0.871253      0.871217   0.869499   \n",
       "4  0.803651  ...      0.801930      0.802308      0.800460        NaN   \n",
       "\n",
       "   gcn4_YPDDiauxic  dal81_Urea  rtg1_Glutamine  rtg1_MinimalGlucose  \\\n",
       "0         0.871985    0.863989        0.860099             0.843983   \n",
       "1         0.811052         NaN             NaN                  NaN   \n",
       "2         0.900743    0.889429        0.888395             0.887932   \n",
       "3         0.885671    0.872017        0.866813             0.855822   \n",
       "4         0.832947    0.804640        0.803859                  NaN   \n",
       "\n",
       "   dal82_YPD             edge  \n",
       "0   0.863186      Q0045-Q0050  \n",
       "1   0.786862      Q0045-Q0075  \n",
       "2   0.892009      Q0045-Q0130  \n",
       "3   0.871797  Q0045-X21S_rRNA  \n",
       "4   0.803727      Q0050-Q0075  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>dal82_MinimalGlucose</th>\n",
       "      <th>dal81_YPDDiauxic</th>\n",
       "      <th>gln3_YPD</th>\n",
       "      <th>stp1_YPDRapa</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal82_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>gat1_YPD</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>stp1_AmmoniumSulfate</th>\n",
       "      <th>...</th>\n",
       "      <th>dal80_YPEtOH</th>\n",
       "      <th>rtg1_CStarve</th>\n",
       "      <th>rtg3_YPDRapa</th>\n",
       "      <th>gln3_YPDRapa</th>\n",
       "      <th>rtg3_Urea</th>\n",
       "      <th>gcn4_YPDDiauxic</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>rtg1_Glutamine</th>\n",
       "      <th>rtg1_MinimalGlucose</th>\n",
       "      <th>dal82_YPD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dal82_MinimalGlucose</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984396</td>\n",
       "      <td>0.989876</td>\n",
       "      <td>0.982914</td>\n",
       "      <td>0.981432</td>\n",
       "      <td>0.982979</td>\n",
       "      <td>0.987297</td>\n",
       "      <td>0.991406</td>\n",
       "      <td>0.992240</td>\n",
       "      <td>0.988140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983024</td>\n",
       "      <td>0.993612</td>\n",
       "      <td>0.986479</td>\n",
       "      <td>0.988645</td>\n",
       "      <td>0.981649</td>\n",
       "      <td>0.979562</td>\n",
       "      <td>0.984980</td>\n",
       "      <td>0.993079</td>\n",
       "      <td>0.987660</td>\n",
       "      <td>0.991389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal81_YPDDiauxic</th>\n",
       "      <td>0.984396</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988442</td>\n",
       "      <td>0.977615</td>\n",
       "      <td>0.975156</td>\n",
       "      <td>0.992956</td>\n",
       "      <td>0.981909</td>\n",
       "      <td>0.986833</td>\n",
       "      <td>0.986625</td>\n",
       "      <td>0.985935</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983683</td>\n",
       "      <td>0.984157</td>\n",
       "      <td>0.981168</td>\n",
       "      <td>0.982902</td>\n",
       "      <td>0.979472</td>\n",
       "      <td>0.989858</td>\n",
       "      <td>0.980638</td>\n",
       "      <td>0.988623</td>\n",
       "      <td>0.983122</td>\n",
       "      <td>0.986981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gln3_YPD</th>\n",
       "      <td>0.989876</td>\n",
       "      <td>0.988442</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985191</td>\n",
       "      <td>0.981535</td>\n",
       "      <td>0.990024</td>\n",
       "      <td>0.989146</td>\n",
       "      <td>0.995828</td>\n",
       "      <td>0.992182</td>\n",
       "      <td>0.992189</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988512</td>\n",
       "      <td>0.989571</td>\n",
       "      <td>0.988331</td>\n",
       "      <td>0.990008</td>\n",
       "      <td>0.986800</td>\n",
       "      <td>0.987310</td>\n",
       "      <td>0.988311</td>\n",
       "      <td>0.993501</td>\n",
       "      <td>0.987810</td>\n",
       "      <td>0.996069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stp1_YPDRapa</th>\n",
       "      <td>0.982914</td>\n",
       "      <td>0.977615</td>\n",
       "      <td>0.985191</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973179</td>\n",
       "      <td>0.978962</td>\n",
       "      <td>0.997316</td>\n",
       "      <td>0.982916</td>\n",
       "      <td>0.984878</td>\n",
       "      <td>0.986357</td>\n",
       "      <td>...</td>\n",
       "      <td>0.980718</td>\n",
       "      <td>0.984367</td>\n",
       "      <td>0.994634</td>\n",
       "      <td>0.993264</td>\n",
       "      <td>0.982349</td>\n",
       "      <td>0.975860</td>\n",
       "      <td>0.981924</td>\n",
       "      <td>0.986258</td>\n",
       "      <td>0.979874</td>\n",
       "      <td>0.982967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <td>0.981432</td>\n",
       "      <td>0.975156</td>\n",
       "      <td>0.981535</td>\n",
       "      <td>0.973179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974698</td>\n",
       "      <td>0.977452</td>\n",
       "      <td>0.981649</td>\n",
       "      <td>0.982387</td>\n",
       "      <td>0.979288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.974986</td>\n",
       "      <td>0.980547</td>\n",
       "      <td>0.977043</td>\n",
       "      <td>0.978636</td>\n",
       "      <td>0.973055</td>\n",
       "      <td>0.971436</td>\n",
       "      <td>0.975866</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>0.977107</td>\n",
       "      <td>0.981658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gcn4_YPDDiauxic</th>\n",
       "      <td>0.979562</td>\n",
       "      <td>0.989858</td>\n",
       "      <td>0.987310</td>\n",
       "      <td>0.975860</td>\n",
       "      <td>0.971436</td>\n",
       "      <td>0.994307</td>\n",
       "      <td>0.980102</td>\n",
       "      <td>0.983545</td>\n",
       "      <td>0.983133</td>\n",
       "      <td>0.985120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.982578</td>\n",
       "      <td>0.979914</td>\n",
       "      <td>0.979351</td>\n",
       "      <td>0.980798</td>\n",
       "      <td>0.979852</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.979410</td>\n",
       "      <td>0.985659</td>\n",
       "      <td>0.980348</td>\n",
       "      <td>0.983975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal81_Urea</th>\n",
       "      <td>0.984980</td>\n",
       "      <td>0.980638</td>\n",
       "      <td>0.988311</td>\n",
       "      <td>0.981924</td>\n",
       "      <td>0.975866</td>\n",
       "      <td>0.982842</td>\n",
       "      <td>0.985677</td>\n",
       "      <td>0.986544</td>\n",
       "      <td>0.987459</td>\n",
       "      <td>0.991611</td>\n",
       "      <td>...</td>\n",
       "      <td>0.985051</td>\n",
       "      <td>0.984507</td>\n",
       "      <td>0.984763</td>\n",
       "      <td>0.986265</td>\n",
       "      <td>0.988442</td>\n",
       "      <td>0.979410</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988341</td>\n",
       "      <td>0.981792</td>\n",
       "      <td>0.986740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rtg1_Glutamine</th>\n",
       "      <td>0.993079</td>\n",
       "      <td>0.988623</td>\n",
       "      <td>0.993501</td>\n",
       "      <td>0.986258</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>0.988664</td>\n",
       "      <td>0.990319</td>\n",
       "      <td>0.993068</td>\n",
       "      <td>0.996127</td>\n",
       "      <td>0.992310</td>\n",
       "      <td>...</td>\n",
       "      <td>0.987689</td>\n",
       "      <td>0.993099</td>\n",
       "      <td>0.989502</td>\n",
       "      <td>0.991335</td>\n",
       "      <td>0.986353</td>\n",
       "      <td>0.985659</td>\n",
       "      <td>0.988341</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989078</td>\n",
       "      <td>0.993048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rtg1_MinimalGlucose</th>\n",
       "      <td>0.987660</td>\n",
       "      <td>0.983122</td>\n",
       "      <td>0.987810</td>\n",
       "      <td>0.979874</td>\n",
       "      <td>0.977107</td>\n",
       "      <td>0.982838</td>\n",
       "      <td>0.983719</td>\n",
       "      <td>0.986904</td>\n",
       "      <td>0.987898</td>\n",
       "      <td>0.986110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.982325</td>\n",
       "      <td>0.986100</td>\n",
       "      <td>0.983349</td>\n",
       "      <td>0.984863</td>\n",
       "      <td>0.980359</td>\n",
       "      <td>0.980348</td>\n",
       "      <td>0.981792</td>\n",
       "      <td>0.989078</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal82_YPD</th>\n",
       "      <td>0.991389</td>\n",
       "      <td>0.986981</td>\n",
       "      <td>0.996069</td>\n",
       "      <td>0.982967</td>\n",
       "      <td>0.981658</td>\n",
       "      <td>0.987127</td>\n",
       "      <td>0.987660</td>\n",
       "      <td>0.999453</td>\n",
       "      <td>0.991948</td>\n",
       "      <td>0.989881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.985454</td>\n",
       "      <td>0.990093</td>\n",
       "      <td>0.987024</td>\n",
       "      <td>0.989292</td>\n",
       "      <td>0.983543</td>\n",
       "      <td>0.983975</td>\n",
       "      <td>0.986740</td>\n",
       "      <td>0.993048</td>\n",
       "      <td>0.986959</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      dal82_MinimalGlucose  dal81_YPDDiauxic  gln3_YPD  \\\n",
       "dal82_MinimalGlucose              1.000000          0.984396  0.989876   \n",
       "dal81_YPDDiauxic                  0.984396          1.000000  0.988442   \n",
       "gln3_YPD                          0.989876          0.988442  1.000000   \n",
       "stp1_YPDRapa                      0.982914          0.977615  0.985191   \n",
       "dal81_MinimalEtOH                 0.981432          0.975156  0.981535   \n",
       "...                                    ...               ...       ...   \n",
       "gcn4_YPDDiauxic                   0.979562          0.989858  0.987310   \n",
       "dal81_Urea                        0.984980          0.980638  0.988311   \n",
       "rtg1_Glutamine                    0.993079          0.988623  0.993501   \n",
       "rtg1_MinimalGlucose               0.987660          0.983122  0.987810   \n",
       "dal82_YPD                         0.991389          0.986981  0.996069   \n",
       "\n",
       "                      stp1_YPDRapa  dal81_MinimalEtOH  dal82_YPDDiauxic  \\\n",
       "dal82_MinimalGlucose      0.982914           0.981432          0.982979   \n",
       "dal81_YPDDiauxic          0.977615           0.975156          0.992956   \n",
       "gln3_YPD                  0.985191           0.981535          0.990024   \n",
       "stp1_YPDRapa              1.000000           0.973179          0.978962   \n",
       "dal81_MinimalEtOH         0.973179           1.000000          0.974698   \n",
       "...                            ...                ...               ...   \n",
       "gcn4_YPDDiauxic           0.975860           0.971436          0.994307   \n",
       "dal81_Urea                0.981924           0.975866          0.982842   \n",
       "rtg1_Glutamine            0.986258           0.983239          0.988664   \n",
       "rtg1_MinimalGlucose       0.979874           0.977107          0.982838   \n",
       "dal82_YPD                 0.982967           0.981658          0.987127   \n",
       "\n",
       "                      WT(ho)_YPDRapa  gat1_YPD  dal81_Glutamine  \\\n",
       "dal82_MinimalGlucose        0.987297  0.991406         0.992240   \n",
       "dal81_YPDDiauxic            0.981909  0.986833         0.986625   \n",
       "gln3_YPD                    0.989146  0.995828         0.992182   \n",
       "stp1_YPDRapa                0.997316  0.982916         0.984878   \n",
       "dal81_MinimalEtOH           0.977452  0.981649         0.982387   \n",
       "...                              ...       ...              ...   \n",
       "gcn4_YPDDiauxic             0.980102  0.983545         0.983133   \n",
       "dal81_Urea                  0.985677  0.986544         0.987459   \n",
       "rtg1_Glutamine              0.990319  0.993068         0.996127   \n",
       "rtg1_MinimalGlucose         0.983719  0.986904         0.987898   \n",
       "dal82_YPD                   0.987660  0.999453         0.991948   \n",
       "\n",
       "                      stp1_AmmoniumSulfate  ...  dal80_YPEtOH  rtg1_CStarve  \\\n",
       "dal82_MinimalGlucose              0.988140  ...      0.983024      0.993612   \n",
       "dal81_YPDDiauxic                  0.985935  ...      0.983683      0.984157   \n",
       "gln3_YPD                          0.992189  ...      0.988512      0.989571   \n",
       "stp1_YPDRapa                      0.986357  ...      0.980718      0.984367   \n",
       "dal81_MinimalEtOH                 0.979288  ...      0.974986      0.980547   \n",
       "...                                    ...  ...           ...           ...   \n",
       "gcn4_YPDDiauxic                   0.985120  ...      0.982578      0.979914   \n",
       "dal81_Urea                        0.991611  ...      0.985051      0.984507   \n",
       "rtg1_Glutamine                    0.992310  ...      0.987689      0.993099   \n",
       "rtg1_MinimalGlucose               0.986110  ...      0.982325      0.986100   \n",
       "dal82_YPD                         0.989881  ...      0.985454      0.990093   \n",
       "\n",
       "                      rtg3_YPDRapa  gln3_YPDRapa  rtg3_Urea  gcn4_YPDDiauxic  \\\n",
       "dal82_MinimalGlucose      0.986479      0.988645   0.981649         0.979562   \n",
       "dal81_YPDDiauxic          0.981168      0.982902   0.979472         0.989858   \n",
       "gln3_YPD                  0.988331      0.990008   0.986800         0.987310   \n",
       "stp1_YPDRapa              0.994634      0.993264   0.982349         0.975860   \n",
       "dal81_MinimalEtOH         0.977043      0.978636   0.973055         0.971436   \n",
       "...                            ...           ...        ...              ...   \n",
       "gcn4_YPDDiauxic           0.979351      0.980798   0.979852         1.000000   \n",
       "dal81_Urea                0.984763      0.986265   0.988442         0.979410   \n",
       "rtg1_Glutamine            0.989502      0.991335   0.986353         0.985659   \n",
       "rtg1_MinimalGlucose       0.983349      0.984863   0.980359         0.980348   \n",
       "dal82_YPD                 0.987024      0.989292   0.983543         0.983975   \n",
       "\n",
       "                      dal81_Urea  rtg1_Glutamine  rtg1_MinimalGlucose  \\\n",
       "dal82_MinimalGlucose    0.984980        0.993079             0.987660   \n",
       "dal81_YPDDiauxic        0.980638        0.988623             0.983122   \n",
       "gln3_YPD                0.988311        0.993501             0.987810   \n",
       "stp1_YPDRapa            0.981924        0.986258             0.979874   \n",
       "dal81_MinimalEtOH       0.975866        0.983239             0.977107   \n",
       "...                          ...             ...                  ...   \n",
       "gcn4_YPDDiauxic         0.979410        0.985659             0.980348   \n",
       "dal81_Urea              1.000000        0.988341             0.981792   \n",
       "rtg1_Glutamine          0.988341        1.000000             0.989078   \n",
       "rtg1_MinimalGlucose     0.981792        0.989078             1.000000   \n",
       "dal82_YPD               0.986740        0.993048             0.986959   \n",
       "\n",
       "                      dal82_YPD  \n",
       "dal82_MinimalGlucose   0.991389  \n",
       "dal81_YPDDiauxic       0.986981  \n",
       "gln3_YPD               0.996069  \n",
       "stp1_YPDRapa           0.982967  \n",
       "dal81_MinimalEtOH      0.981658  \n",
       "...                         ...  \n",
       "gcn4_YPDDiauxic        0.983975  \n",
       "dal81_Urea             0.986740  \n",
       "rtg1_Glutamine         0.993048  \n",
       "rtg1_MinimalGlucose    0.986959  \n",
       "dal82_YPD              1.000000  \n",
       "\n",
       "[132 rows x 132 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs = df.iloc[:,2:-1].dropna().corr()\n",
    "corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "details = bonobo_names_df.loc[corrs.index,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_117616/1976088629.py:4: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = matplotlib.cm.get_cmap('Set1', len(colors))\n",
      "/tmp/ipykernel_117616/1976088629.py:10: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap2 = matplotlib.cm.get_cmap('plasma', len(colors2))\n",
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "colors = details.medium.unique()\n",
    "cmap = matplotlib.cm.get_cmap('Set1', len(colors))\n",
    "color_list = [matplotlib.colors.rgb2hex(cmap(i)[:3]) for i in range(cmap.N)]\n",
    "lut = dict(zip(colors, color_list))\n",
    "row_colors = details.medium.map(lut)\n",
    "\n",
    "colors2 = details.genotype.unique()\n",
    "cmap2 = matplotlib.cm.get_cmap('plasma', len(colors2))\n",
    "color_list2 = [matplotlib.colors.rgb2hex(cmap2(i)[:3]) for i in range(cmap2.N)]\n",
    "lut2 = dict(zip(colors2, color_list2))\n",
    "row_colors2 = details.genotype.map(lut2)\n",
    "\n",
    "# add name to cbar\n",
    "f1 = sns.clustermap(corrs, row_colors=[row_colors, row_colors2], cmap = 'jet', \n",
    "                    figsize=(15,15), cbar_pos=(0.02, 0.80, 0.05, 0.18), cbar_kws= {'label':'Pearson Corr'}, xticklabels=False, yticklabels=False)\n",
    "\n",
    "f1.ax_col_dendrogram.set_visible(False)\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "handles = [Patch(facecolor=lut[name]) for name in lut]\n",
    "l1 = plt.legend(handles, lut, title='Media',\n",
    "           bbox_to_anchor=(.3, .985), bbox_transform=plt.gcf().transFigure, loc='upper right')\n",
    "\n",
    "# change the location \n",
    "handles = [Patch(facecolor=lut2[name]) for name in lut2]\n",
    "l2 = plt.legend(handles, lut2, title='Genotype',\n",
    "           bbox_to_anchor=(.4, .985), bbox_transform=plt.gcf().transFigure, loc='upper right')\n",
    "\n",
    "# Move the legends\n",
    "plt.gca().add_artist(l1)\n",
    "plt.gca().add_artist(l2)\n",
    "\n",
    "f1.savefig(results_folder+'/bonobo_correlation_clustermap.pdf', bbox_extra_artists=(l1,l2), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actual Expression Values and Perturbed networks"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Engineered strains\n",
    "Therefore, we constructed an array of prototrophic, diploid yeast strains with homozygous deletions of TFs that control\n",
    "distinct regulatory modules: \n",
    "\n",
    "1) NCR, The Nitrogen Catabolite Repression (NCR) pathway [dal80, gat1, gln1, gfz3], which is regulated principally by TORC1, consists of the TFs _GAT1_, _GLN3_, _DAL80_, and _GZF3_ ([Hofman-Bang, 1999](https://elifesciences.org/articles/51254#bib52)), and is responsible for suppressing the utilization of non-preferred nitrogen sources when preferred nitrogen sources are available.\n",
    "2) GAAC, The General Amino Acid Control (GAAC) pathway consists of the TF [_GCN4_] ([Hinnebusch, 2005](https://elifesciences.org/articles/51254#bib51)), and is responsible for activating the response to amino acid starvation, as detected by increases in uncharged tRNA levels.\n",
    "3) SPS-sensing, The Ssy1-Ptr3-Ssy5-sensing (SPS) pathway ([Ljungdahl, 2009](https://elifesciences.org/articles/51254#bib71)), consists of the TFs [_STP1_ and _STP2_], and is responsible for altering transporter expression\n",
    "4) the retrograde pathway [rtg1, rtg3] that coordinately control nitrogen-related gene expression in yeast, The retrograde pathway, consisting of the TF heterodimer _RTG1_ and _RTG3_, is responsible for altering expression of metabolic and biosynthetic genes in response to mitochondrial stress ([Jia et al., 1997](https://elifesciences.org/articles/51254#bib60); [Liao and Butow, 1993](https://elifesciences.org/articles/51254#bib70)) or environmental stress ([Ruiz-Roig et al., 2012](https://elifesciences.org/articles/51254#bib97))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "deletion_genes = ['dal80','dal81','dal82','gat1', 'gln3', 'gzf3', 'gcn4', 'stp1', 'stp2', 'rtg1', 'rtg3']\n",
    "#deletion_genes = ['dal80','gat1', 'gln3', 'gzf3', 'gcn4', 'stp1', 'stp2', 'rtg1', 'rtg3']\n",
    "\n",
    "gene_maps = {'dal81':'YIR023W', 'dal82':'YNL314W','dal80':'YKR034W','gat1':'YFL021W', 'gln3':'YER040W', 'gzf3':'YJL110C', 'gcn4':'YEL009C', 'stp1':'YDR463W', 'stp2':'YHR006W', 'rtg1':'YOL067C', 'rtg3':'YBL103C' }\n",
    "# gene_maps = {'dal80':'YKR034W','gat1':'YFL021W', 'gln3':'YER040W', 'gzf3':'YJL110C', 'gcn4':'YEL009C', 'stp1':'YDR463W', 'stp2':'YHR006W', 'rtg1':'YOL067C', 'rtg3':'YBL103C' }\n",
    "deletion_ids = [gene_maps[i] for i in deletion_genes]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with open(\n",
    "#    '../../bonobo_paper_yeast/data/processed_data/GSE125162_all_pseudobulk_logcounts.txt','r') as f:\n",
    "#    expr_old = pd.read_csv(f, sep = '\\t', index_col = 0)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WT(ho)_AmmoniumSulfate</th>\n",
       "      <th>WT(ho)_CStarve</th>\n",
       "      <th>WT(ho)_Glutamine</th>\n",
       "      <th>WT(ho)_MinimalEtOH</th>\n",
       "      <th>WT(ho)_MinimalGlucose</th>\n",
       "      <th>WT(ho)_Proline</th>\n",
       "      <th>WT(ho)_Urea</th>\n",
       "      <th>WT(ho)_YPD</th>\n",
       "      <th>WT(ho)_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>...</th>\n",
       "      <th>stp2_CStarve</th>\n",
       "      <th>stp2_Glutamine</th>\n",
       "      <th>stp2_MinimalEtOH</th>\n",
       "      <th>stp2_MinimalGlucose</th>\n",
       "      <th>stp2_Proline</th>\n",
       "      <th>stp2_Urea</th>\n",
       "      <th>stp2_YPD</th>\n",
       "      <th>stp2_YPDDiauxic</th>\n",
       "      <th>stp2_YPDRapa</th>\n",
       "      <th>stp2_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YDL248W</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.003180</td>\n",
       "      <td>0.002144</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.006494</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>YDL247W</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>YDL246C</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL245C</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL244W</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012474</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006780</td>\n",
       "      <td>0.001024</td>\n",
       "      <td>0.028259</td>\n",
       "      <td>0.022282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035091</td>\n",
       "      <td>0.012073</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.012945</td>\n",
       "      <td>0.024693</td>\n",
       "      <td>0.061875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q0255</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q0275</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPM1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KANMX</th>\n",
       "      <td>2.360386</td>\n",
       "      <td>1.014260</td>\n",
       "      <td>2.011293</td>\n",
       "      <td>1.759183</td>\n",
       "      <td>2.200487</td>\n",
       "      <td>1.268030</td>\n",
       "      <td>2.427748</td>\n",
       "      <td>1.901312</td>\n",
       "      <td>1.486226</td>\n",
       "      <td>1.456467</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001357</td>\n",
       "      <td>2.033365</td>\n",
       "      <td>2.207096</td>\n",
       "      <td>2.372927</td>\n",
       "      <td>1.500772</td>\n",
       "      <td>2.615808</td>\n",
       "      <td>1.996838</td>\n",
       "      <td>1.457781</td>\n",
       "      <td>1.563132</td>\n",
       "      <td>2.406881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NATMX</th>\n",
       "      <td>2.588820</td>\n",
       "      <td>1.358721</td>\n",
       "      <td>2.786845</td>\n",
       "      <td>1.978345</td>\n",
       "      <td>2.986127</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>2.659736</td>\n",
       "      <td>3.098374</td>\n",
       "      <td>1.951807</td>\n",
       "      <td>2.841197</td>\n",
       "      <td>...</td>\n",
       "      <td>1.343930</td>\n",
       "      <td>2.772589</td>\n",
       "      <td>2.268068</td>\n",
       "      <td>3.187456</td>\n",
       "      <td>1.753067</td>\n",
       "      <td>2.841871</td>\n",
       "      <td>3.260749</td>\n",
       "      <td>2.014251</td>\n",
       "      <td>3.067180</td>\n",
       "      <td>3.015396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6520 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         WT(ho)_AmmoniumSulfate  WT(ho)_CStarve  WT(ho)_Glutamine  \\\n",
       "YDL248W                0.000000        0.000000          0.000000   \n",
       "YDL247W                0.000000        0.000000          0.000000   \n",
       "YDL246C                0.000000        0.000000          0.000000   \n",
       "YDL245C                0.000000        0.004175          0.000000   \n",
       "YDL244W                0.000000        0.012474          0.006734   \n",
       "...                         ...             ...               ...   \n",
       "Q0255                  0.000000        0.000000          0.000000   \n",
       "Q0275                  0.000000        0.000000          0.000000   \n",
       "RPM1                   0.000000        0.000000          0.000000   \n",
       "KANMX                  2.360386        1.014260          2.011293   \n",
       "NATMX                  2.588820        1.358721          2.786845   \n",
       "\n",
       "         WT(ho)_MinimalEtOH  WT(ho)_MinimalGlucose  WT(ho)_Proline  \\\n",
       "YDL248W            0.000000               0.004890        0.015267   \n",
       "YDL247W            0.000000               0.000000        0.000000   \n",
       "YDL246C            0.000000               0.000000        0.000000   \n",
       "YDL245C            0.000000               0.000000        0.000000   \n",
       "YDL244W            0.000000               0.000000        0.000000   \n",
       "...                     ...                    ...             ...   \n",
       "Q0255              0.000000               0.000000        0.000000   \n",
       "Q0275              0.000000               0.004890        0.000000   \n",
       "RPM1               0.000000               0.000000        0.000000   \n",
       "KANMX              1.759183               2.200487        1.268030   \n",
       "NATMX              1.978345               2.986127        1.757858   \n",
       "\n",
       "         WT(ho)_Urea  WT(ho)_YPD  WT(ho)_YPDDiauxic  WT(ho)_YPDRapa  ...  \\\n",
       "YDL248W     0.000000    0.000000           0.003180        0.002144  ...   \n",
       "YDL247W     0.000000    0.000000           0.000000        0.000000  ...   \n",
       "YDL246C     0.000000    0.000000           0.000000        0.000000  ...   \n",
       "YDL245C     0.000000    0.000000           0.000000        0.002144  ...   \n",
       "YDL244W     0.006780    0.001024           0.028259        0.022282  ...   \n",
       "...              ...         ...                ...             ...  ...   \n",
       "Q0255       0.000000    0.000000           0.000000        0.000000  ...   \n",
       "Q0275       0.000000    0.000000           0.003180        0.000000  ...   \n",
       "RPM1        0.000000    0.000000           0.000000        0.000000  ...   \n",
       "KANMX       2.427748    1.901312           1.486226        1.456467  ...   \n",
       "NATMX       2.659736    3.098374           1.951807        2.841197  ...   \n",
       "\n",
       "         stp2_CStarve  stp2_Glutamine  stp2_MinimalEtOH  stp2_MinimalGlucose  \\\n",
       "YDL248W      0.000000        0.000000          0.000000             0.004040   \n",
       "YDL247W      0.000000        0.000000          0.000000             0.000000   \n",
       "YDL246C      0.000000        0.000000          0.000000             0.000000   \n",
       "YDL245C      0.000000        0.000000          0.017700             0.000000   \n",
       "YDL244W      0.017778        0.000000          0.035091             0.012073   \n",
       "...               ...             ...               ...                  ...   \n",
       "Q0255        0.000000        0.000000          0.000000             0.000000   \n",
       "Q0275        0.000000        0.000000          0.000000             0.000000   \n",
       "RPM1         0.000000        0.000000          0.000000             0.000000   \n",
       "KANMX        1.001357        2.033365          2.207096             2.372927   \n",
       "NATMX        1.343930        2.772589          2.268068             3.187456   \n",
       "\n",
       "         stp2_Proline  stp2_Urea  stp2_YPD  stp2_YPDDiauxic  stp2_YPDRapa  \\\n",
       "YDL248W      0.009852   0.000000  0.000000         0.006494      0.003743   \n",
       "YDL247W      0.000000   0.000000  0.000000         0.000000      0.000000   \n",
       "YDL246C      0.000000   0.000000  0.000000         0.000000      0.000000   \n",
       "YDL245C      0.000000   0.000000  0.000000         0.000000      0.001249   \n",
       "YDL244W      0.009852   0.000000  0.001278         0.012945      0.024693   \n",
       "...               ...        ...       ...              ...           ...   \n",
       "Q0255        0.000000   0.000000  0.000000         0.000000      0.000000   \n",
       "Q0275        0.000000   0.000000  0.000000         0.006494      0.001249   \n",
       "RPM1         0.000000   0.000000  0.000000         0.000000      0.000000   \n",
       "KANMX        1.500772   2.615808  1.996838         1.457781      1.563132   \n",
       "NATMX        1.753067   2.841871  3.260749         2.014251      3.067180   \n",
       "\n",
       "         stp2_YPEtOH  \n",
       "YDL248W     0.000000  \n",
       "YDL247W     0.000000  \n",
       "YDL246C     0.000000  \n",
       "YDL245C     0.000000  \n",
       "YDL244W     0.061875  \n",
       "...              ...  \n",
       "Q0255       0.000000  \n",
       "Q0275       0.000000  \n",
       "RPM1        0.000000  \n",
       "KANMX       2.406881  \n",
       "NATMX       3.015396  \n",
       "\n",
       "[6520 rows x 132 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.5,  11.5,  22.5,  33.5,  44.5,  55.5,  66.5,  77.5,  88.5,\n",
       "        99.5, 110.5, 121.5])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[0].get_yticks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['WT(ho)',\n",
       " 'dal80',\n",
       " 'dal81',\n",
       " 'dal82',\n",
       " 'gat1',\n",
       " 'gcn4',\n",
       " 'gln3',\n",
       " 'gzf3',\n",
       " 'rtg1',\n",
       " 'rtg3',\n",
       " 'stp1',\n",
       " 'stp2']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/new_pseudobulk_perc02/gcn4/'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MQFNswoQJQev5m1MRERGmSZMmZt26dYF5/nH0iWP0Hj16mJ07dwaWmTJlipF+uzC7b9++Bc6PjY0N+fz+9ddfm9mzZ5usrKyg6b/88osZMGCAkWSefPLJAl+rojT6T+XNN980kkxycnLQxdAlqRsYY8yrr75qJJmkpCTz6aefBqbv3r3bdO/ePTBuKotGvzHGfPXVVyYqKspERkaaZcuWGWN+GxN36tQp0JQ/2YljsZPH4W+99VZgDD537tyg9fyvfUREhOnUqVPQGMr/e1SScXVx6yv9+vULvMYn113Wr19vXn/99aBpBTV7T/f9FBUVZZo3b26+++67wLxvv/02UM86eb1TKWkWY059M8OpfPPNN4Fz/eyzz5r8/HxjzG837QwfPjzwu3xyY97n85nu3bsbSWbAgAHm559/Dszbu3evufTSSwM1nhOdqtF//fXXG+m3m3rWr18fmH706FFz0003GUmmU6dOIesVVGvxc8K5P3r0aOAimxtuuMEcPXo0MO/f//63iYmJCZyL0qqL+V+3uLg4M3HixMBNW9nZ2YE6VdeuXUOylmR/d955p5FkLrzwQrN///6g7W3fvt08//zzRX6tAKA8odEPoFzyD/YK+wk38Pz888+N1+s10dHRZu3atWb+/PnG4/GY2NjYoA++fv4BoiSzevXqkPnTp08PXMl64sDN/yFdCr7D/kRXXnllyEUHJ/rggw+M9NsTCPz+9a9/GUnm8ssvL+xlMsYYM2zYMCPJ/O1vfyvS8saEH4Bs2rQpcPe2f1B+osWLFxvptyuaT7wYwj9AkmTGjh0bst6aNWsCxYyiKmkWY0re6PcPkqpXrx5S+DHGmDvuuCNwnCcOaPbu3WtiY2NNYmJigYPZ/v37G0nmrbfeCpoebhCck5NjatWqZbxer1m+fHnY7d11111Gknn88ceDppe00V8ez31ERITZvHlzyPwFCxYE9nnyOW7fvr2RZGbMmBF22+PHjzeSzE033RQ03f+6tW7dOlBQOFFqaqqRZD744IPT3t9TTz1lJJm777477DoAAABAWTi50b9ixYpAs+XExmdBjX5/07patWpBzSC/xx9/3EgyDRs2DHrann/cJRX8JCv/PmNjY0OarcaYQOM4Li7ulPNP/vx+KpmZmSY6Otq0aNEiZF5pNPqXLFlioqOjTURERFDT2ZiS1Q18Pl+gORiuOfXLL78EnghY0kZ/YT/hjBkzxkgyTZs2NRkZGYGGW4sWLUKekmjM/17bwsbhJzdH/a99TExM2N+Bko6ri1tf8T/FYO3atUVavqBm7+m+nzweT9jj9I9J+/fvX6R8p5PFmJI3+v11nH79+oXM8/l8plWrVmEb87NnzzaSTLt27UxOTk7IuhkZGaZOnTrG6/Wabdu2BaYX1Ohft26dkX67a//gwYMh28vPzw88iWTx4sVB80ra6C9P5/6NN94w0m9PyAz3ej766KOlXhfzby/chUC7d+8O/B078XyUdH/+C9pOfpIHADidVwBQjtWrV0/p6ekF/lSuXDlkne7du+uBBx5QTk6OrrvuOg0ePFjGGI0dO1bnnntugfvq2rWr2rVrFzJ9wIABql27to4ePaovvvgiZH6lSpV0xRVXhEzPycnRf/7zH3m9Xt14441h93nppZcqKipKX3zxhfLy8gLHLEnLly8v0vfq+ZefM2eOjh49WujyBZk3b56MMerUqZM6d+4cMr9bt27q0KGDjDGaN29e2G3ccsstIdPatm2r2NhYHTp0qMjfD1YaWYpr7ty5kqSbbrpJsbGxIfOHDx8edr2PPvpI2dnZuuCCCwLfVX8y/+/HwoULC83x5ZdfateuXWrTpo06dep02tsrivJ47nv06KEmTZqEzO/du7caNWoUMn3Hjh1atWqVqlatqgEDBoTddmGv20033SSvN/Sjkf81+fHHH097f/736/z587Vv376w6wEAAABlrWPHjurXr5+ysrL01FNPFbr8xx9/LOm3z9AJCQkh84cPH67IyEht3bpVGzZsCJnfsmVLdejQ4ZT76Nu3r+rWrRsyPS0trUjzT/z87pebm6t3331Xt956q/r27avu3burW7du6tOnjzwejzZs2KCsrKxT5iqu7du3a8CAAcrJydG4ceN04YUXBuaVtG6wfv16bd26VdHR0frTn/4Usk7t2rXD1imKo1WrVqesx4Tz0EMPqVu3btq8ebP69++vJ598UlFRUXr77bcVFxdX4L4KG4cvWbJEx44dC5l/wQUXhP0dKOm4urj1Ff/y7777rnw+X6HLF+R030+pqalhjzPcWPZMZykJ/z7vuOOOkHkejyfsdEl6//33JUmDBw9WVFRUyPzExET16dNHPp+v0O96P3F7AwcOVHJycsh8r9erfv36SSq9ekx5OveffPKJJOmGG24I+3oW9DeqNOpi4WpKNWrUCGzvxOMo6f7879f3339fOTk5YdcDACeKtB0AAE7lxhtv1KhRo4q93ujRozVv3jytXr1aknTRRRcV2Kj1O+ecc8JO93q9at68uXbu3KkNGzbooosuCprfrFkzRUREhKy3adMmZWdnKyoqSpdddlmB+/V4PMrOztb+/ftVo0YNde3aVV26dNGyZcvUrFkz9erVS7169QoMpk/+sD106FA9++yzmj9/vurUqaO+ffsqPT1dvXr1Ups2bU55zCfauHGjpN8KLgVp1aqVvvrqq8CyJ0pJSQl74YUknXXWWdqxY4eOHj2qatWqnfEsJeEfMBX0e9CsWTNFRkYGCit+69atkyR99dVX6tatW9h1Dx06JEn65ZdfCs3h397PP/9c4Pays7OLvL2iKI/n/lS/u61btw65COabb76R9Fvhrnv37mHXM8ZIKvh1a9q0adjpNWrUkKSgQk9J93f55ZerYcOG+vbbb1WvXj316dNHPXr0UPfu3dWpUyd5PJ6w2wIAAADOtNGjR2v27NmaMGGC7r///rANVL/CxhDJycmqW7eutm7dqo0bN6pFixZB8wsad52ooM/n1atXl6SwFwafOP/kRu0vv/yivn376ttvvz3lfg8cOKA6deoUmq8oMjMz1b9/f+3Zs0c33XST7rrrrqD5Ja0b+MevdevWVWJiYth1TnWjQ1G89NJL6tWrV7HWiYiI0FtvvaXU1FQtWLBAkvT4448HLr4oSFHG4T/++GPIOLGg9Uo6ri5ufeXuu+/W/Pnz9eSTT+qtt97SxRdfrPPOO0/nn3/+Kd8/Jzvd91NxxrJnOktxHTp0SL/++qukgs9nQb/L/vP897//XdOnTw+7zLZt2yQVrx4ze/ZsrVy5Muwy/qxlVY+xce4Lqsc0aNBAlSpV0pEjR4Kml0Zd7FTHsXHjxqDjKOn+7rjjDv3jH//Q1KlTNXfuXF188cVKT0/X7373uwL3DwBOQKMfQIUUFRWlbt26BRr9N910U6ENNH8xIBz/B+SMjIyQeeGuuJX+96EyNzc37JMATua/a8Dr9Wru3LkaPXq0pk2bpvnz52v+/PmSfmuaPvDAA7r33nsDdx7Xrl1by5Yt06OPPqo5c+bo3Xff1bvvvivpt8HQ2LFjdckllxS6f/+HZv+xhlOS18F/TNL/Gp9nOktJ+PdZ0O+B1+tVSkqKdu/eHTTdf55//vln/fzzz6fcR1HuDPFvb9++fYXe8V1ad5qUx3OflJRU4DLh5vlft4yMjELfb/6CzskKOo5wx1DS/SUkJGjJkiUaOXKkpk+frtmzZ2v27NmSpPr16+uxxx7TkCFDTrk9AAAA4ExITU3VwIEDNX36dI0ZM0avv/56gcsWdQyxdevWYo8hClvGP7YvbP7JY5AhQ4bo22+/VceOHTVq1CilpaWpWrVqgYvp69evrx07dig3N7fQbEVhjNHgwYO1du1ade/eXa+++mrIMiWtGxQ2fpVOfW7OpHr16qlp06ZavXq1IiMjNXjw4ELXKco4vCT1mOKOq4tbX7nkkks0d+5cPfHEE1q6dKkmTpyoiRMnyuPxqE+fPvrb3/5WpItaztT7qbjj8dLIUlwnNnAL+j0oKIv/PH/33XeF7qc49Zgff/yx0Dvhy7oeU5bnvrB6zMmN/tKoi5WkHlPc/bVt21aLFy/WY489pvnz52vq1KmaOnWqJKlLly56/vnn1bVr11NuDwDKIx7dD6BCWrRokV5++eXAB8J77rlHBw8ePOU6e/fuLXCe/4rdU33YPZn/qvoaNWrIGFPoz4mPm0pOTtbzzz+vX3/9VevWrdMrr7yivn37at++fbr//vv17LPPBu2rRYsWevfdd3Xo0CEtWbJEY8aMUWpqqr7//nv169dPS5cuLXJe/7GW1utQEjay+PdZ0O+Bz+cLWyDwr/fggw8Weo6L8mg3//auueaaQre3devWkh1sAfssT+f+VAWDcPP863Xu3LlI77fSylmS/dWpU0dvvPGGDhw4oK+++krPPfec0tPTtX37dg0dOlT//ve/TzsfAAAAUBKPPfaYvF6vJk2adMrxRnkaQxTF7t27NX/+fMXHx+vjjz/W73//e9WsWTPQ5DfG6MCBA6W6z9GjR2vGjBlq0KCB3nvvPUVHR4csU9K6QWHjV+nU5+ZMevLJJ7V69Wp5vV7l5eWF/WqBkxVlHF6SekxJxtXFra/07dtXixcv1oEDBzRnzhzde++9qlmzpubNm6cLLrig0FrUiXnLw/uprLOc+ESKgn4PCsriX3fu3LmFnueiPC3Uv73XXnut0O1NmTKleAdayD7L07kvaT2mtOpiRc1Zkv117txZH330kQ4dOqT58+frkUceUZMmTbRs2TL16dNHP/3002nnA4CyRqMfQIVz+PBhDRo0SD6fT88//7wuvvhi/fLLLxo2bNgp1/vhhx/CTvf5fIHH4jVr1qzIOc4++2xFR0drz549pxx8n4rH41GrVq10++23a+7cuXrxxRclSRMnTgy7fHR0tNLT0/XII49ozZo1GjhwoHw+n958881C9+U/tlNdCe2fV5zXoSRsZGnevLmkgn8PNm3aFPLYful/j1cr7PGPRXU62yvpY9/L47n3Pxo/HP9j2k7kf93Wr1+v/Pz8MxOulPcXERGh9u3b67777tOSJUt03333SSr4/Q0AAACcaeecc46uu+465ebmavTo0QUuV9gY4vDhw4E7Lc/0GKIo/F/91aJFC1WtWjVk/rp168J+B3xJvf/++3rssceUmJioWbNm6ayzzgq7XEnrBv7xq/9r0sIpaGx7Jq1cuVKjR49WVFSUPv74Y9WuXVsfffRR2KcZnKiwcXhERESBX9UQTmmM04tbX6lcubIuueQSjRs3Ths2bFCjRo20c+dOzZkzp9B9laf3U1lnSU5ODtzNXtDvQUHTy1M9pqTK47kvqB6zbdu2kLv5pbJ/3Upjf/Hx8Tr//PM1ZswYrV+/Xl26dNGxY8f09ttvl1ZMACgzNPoBVDh33HGHtm/frj59+ujOO+/UpEmTlJKSounTpwceyRTO0qVLtXbt2pDpH3zwgXbu3KnExMQCv/spnPj4ePXt21fGGP3tb38rwZGE8n8X+K5duwpd1uPxBPIWZfmLLrpIHo9HK1as0PLly0PmL126VCtXrpTH49GFF15YzOTFYyNL3759JUlvvvmmjh8/HjL/5ZdfDrvepZdeqpiYGH3yySf6/vvvTztHt27dVL16dX333Xf69NNPi7VuXFycpOI/Qq48nXv/9hcvXqzNmzeHzF+4cGGgSHeipk2bqk2bNjp8+LAmTZp0RjOeqf0V5/0NAAAAnCkjR45UZGSkpk6dqk2bNoVd5sTxU7gG+SuvvKK8vDw1atQo0JS2yT9W2r17d9gnfD333HOltq+vv/5agwYNkiS99dZbBX7ftVTyukGLFi3UoEED5eTkhG0879q1S++//36xs5+OzMxM3XDDDcrLy9PIkSPVp08fTZkyRR6PRyNGjND69esLXLewcXi3bt2K9HUPfqczrg6nuPWVpKQkpaamFnn58vR+spHloosukqQCLwh55ZVXwk6/8sorJUkTJkxQZmbmaefwb++f//yn9uzZU6x1S1qPKU/n3n8epk2bFvYrTCZPnhx2vdKuixWmtPcXGRmpLl26SKIeA8CZaPQDqFD+/e9/6+2331bVqlUDA8qaNWvq73//uyTpz3/+c4GPH4yKitLgwYODmohr167VnXfeKUm67bbbgh4pVhSPP/644uLi9PTTT+uxxx4LudJ+//79evPNNzVmzJjAtLfffluPPfZYyPeBHTlyRM8884wkKS0tLTD9L3/5iyZOnBjymMGtW7cGjvvE5QvSpEkTXXXVVZKkwYMHB55iIP1217L/e8OvvfZaNW7cuNDtnQ4bWa655hrVr19fv/76q4YOHRp0rt5++21NmDBBkZGRIevVrFlTI0aMUF5envr27at58+aFLLNu3To9+OCDRfrOxdjYWD3xxBOBTO+88458Pl/QMj/++KPGjBkTUrjxvxaLFi0q/IBPUJ7OfdOmTXXFFVcoLy9PV199tbZt2xaYt27dOg0ePDjweM2TjR07Vl6vV3feeadeffVV5eTkBM3fuXOnXnjhhVN+12hxlGR/zz//vJ5//nnt3LkzaNm9e/fqhRdekFS09ysAAABwpjRt2lSDBg1Sfn5+gV8r9Yc//EENGjTQ/v37dcMNNwS+M1mSZs+eHRjjPvTQQyV+8lhpatmypapWraqdO3dq5MiRgTFWbm6uRo4cqWnTpoV9tH5x7d27V/3799exY8c0ZswYXX755YWuU5K6gcfj0f333y/ptwszFixYEJi3Z88e/eEPfyiVrywrjnvvvVcbN25Uenq6HnroIUkK3ICRlZWlG264IWzzUJIOHDgQMg7/5z//GRhLPfDAA8XKUtJxdXHrK9dcc41mzZoVcpHCZ599pvnz54csX5Dy9H6ykeXee+9VZGSkZs6cqfHjxwfOVU5Oju6+++4CLxK5/PLL1a1bN23evFkXX3xxyHJ5eXn67LPPdN1114W9kORk7dq10/XXX6+DBw/q/PPP14oVK4LmG2O0fPly3XbbbSGPeC9pPaY8nftrr71WdevW1aZNm3TTTTcFXXgwffp0Pf3002HrMaVdFytMSfc3bNgw/etf/wr5G/vNN9/o3XfflUQ9BoBDGQAoh3r27GkkmXr16pn09PQCf4YMGRJYZ8eOHaZKlSpGkpk+fXrINm+88UYjyXTr1s3k5+cHpo8cOdJIMsOHDzcNGzY0ERERJjU11Zx77rlGkpFkzjvvPHPs2LGg7X322WdGkunZs+cpj2XOnDkmKSnJSDIxMTGmTZs2plOnTqZhw4bG4/EYSeaaa64JLD9+/PjAfmvWrGk6duxo2rRpY+Li4owkU7lyZfPVV18Flu/fv7+RZDwej2ncuLHp1KmTad68ufF6vUaSadmypTl48GBQJv/2T7Zv3z7Ttm1bI8l4vV7TunVr07p168C20tLSzIEDB4LW2bJli5FkGjRoUOBr0KBBAyPJbNmy5ZSv1elmMcaYwYMHG0lm8uTJRd6X35IlS0x8fLyRZBITE02HDh1MvXr1jCRz5513Fngc+fn55uabbw68rmeddZbp1KmTadeuXeB3UpKZO3dukV+XUaNGBX4/kpOTTYcOHUz79u1N9erVA9t77bXXgtb5/PPPA/OaNWtmevToYXr27Bm0Xyec+127dpkmTZoYSSYyMtK0bdvWtGzZ0ng8HtOxY0dz7bXXGklm6tSpIev+/e9/N1FRUUaSSUhIMO3atTMdO3Y0derUCRz7gw8+GLSO/+/NZ599FjaP/2/EyJEjT3t/d911V2B6vXr1TKdOncy5554b2EadOnWK9VoBAAAAJeH/DFzQuGnr1q0mOjo68Nl18ODBIcusWLEiMN6Ji4szHTp0MI0aNQqsc+ONN4asM3ny5AK353eqz9+nM/+1114LZKtevbrp0KFDIP/IkSMLHLsUNF4Itx//8Xm93lPWMt58882gbRW3bmDMb+PQK664InBMTZs2NWlpaSY6OtpUqVLFPPLII4W+1uH4t9eqVatTHsMLL7wQlF+SSUpKMj/99FPQ9rKyskzLli2NJPPwww+HfW3HjBljYmJiQsbhksxdd90VkrGw3wG/4o6ri1tfqVy5spFkoqOjzbnnnms6deoUlP36668PynOqOtKZeD8VZcweTkmyGFOy8b/fibWwGjVqmI4dO5oqVaoYr9drnn322QKP49dffzWdO3cOrNuoUSPTpUsX06pVKxMbGxuYnpWVFVjnVK/LsWPHzCWXXBJYr06dOqZz584mNTXVJCYmBqb/8MMPQeuNHj3aSDIRERGmXbt2pmfPnqZnz55m165dxhjnnPtFixYF6o9JSUmmY8eOpn79+iF1se3btwetV9K6WEE1Kr+C/v6WZH+pqamBc9SsWTPTuXPnQO1JkunVq5fJyckp1usFAOUBjX4A5ZL/g1xhP6mpqcYYY3w+nzn//PONJDNo0KCw28zIyDCNGzc2kswTTzwRmH7iAPHXX381t9xyi6lTp46Jjo42jRs3No8++qjJzMwM2V5RG/3G/HYRwogRI0zLli1NQkKCiY+PN2effbb5/e9/b1577TWze/fuwLLbt283Y8eONRdddJFp0KCBiYuLMzExMaZZs2bmjjvuMNu2bQva9sqVK83DDz9sunbtamrXrm2ioqJMYmKiad++vXnyySfN0aNHQ/Kc6oP0sWPHzJNPPmlSU1NNfHy8iY+PN6mpqebpp58O+zqcqWZvSbIYc3qNfmOM+e6778yAAQNMlSpVTFxcnElNTTWvv/56kY7j008/NQMHDgych2rVqpnU1FRz8803m48++ihkwFDY9lasWGEGDRpkGjRoYGJiYkxycrJp1aqVue6668yMGTNCLj4xxph//vOfplOnTiYhISFwnk98LZxy7vft22fuuOOOwHuxYcOGZsSIEebo0aNm4MCBRpKZOXNm2HXXr19vbr31VnP22WebuLg4k5SUZFq0aGEGDBhg/vGPf4Rc+HI6jf7i7u+HH34wo0ePNj179jT16tUzMTExJj4+3rRq1co89NBDZt++fcV6nQAAAICSKKzRb4wxt9122ykb/cb8Nta94447TKNGjUx0dLRJTk42vXr1Mu+8807Y5W02+o0x5p133jHt27c3MTExpnLlyqZbt26BrKXZ6C/sJ1y24tQN/PLy8szzzz9vzj33XBMTE2Nq1Khh/vCHP5iffvqpSK91OEXJf2IDfs+ePaZGjRpGkpk0aVLYba5Zs8ZER0cbr9drFi9eHPa1XbVqlbnssstM1apVTWxsrElLSzNTpkwJu72iNvqNKd64urj1lZkzZ5pbb73VtGnTxqSkpJjIyEhTrVo1c/7555u33nrL+Hy+oOULqyOV9vuppM3ekmQx5vQa/cYYM3v2bNOtWzeTkJBgKleubM4//3yzYMGCQo8jJyfHvPHGG+b888831apVM1FRUaZ27dqmS5cu5qGHHjIrV64MWr6w7fl8PjN9+nRzySWXmOrVq5uoqChTo0YN0759e3PnnXeaRYsWBd1A5M8wcuRI07x5cxMTExN4n/hfCyed+2+++cZcccUVQXWxV1991RhjTEpKipFkDh06FHbd4tbFStroL8n+FixYYO655x7Tvn17U6NGDRMZGWkqV65s0tPTzcsvv0yTH4BjeYwp4+c4AUA5M2rUKD322GMaOXKkRo0aZTsOgEK0bt1a3377rdauXRv43kMAAAAAgLP06tVLixYt0meffaZevXrZjgPgFPbv36+UlBRVqVIl5OstAAD2eG0HAAAAKKqVK1fq22+/VdWqVdWyZUvbcQAAAAAAACq8yZMnS5K6detmOQkA4EQ0+gEAQLmyd+9ePfXUU9q7d2/Q9GXLlunqq6+WJP3pT39SZGSkjXgAAAAAAAAVzqJFizRt2jRlZ2cHpvl8Pk2dOlV//etfJUm33367rXgAgDCokAMAgHLl+PHj+stf/qJHHnlEtWvXVp06dbRr1y7t2LFDknTeeefp0UcftZwSAAA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",
      "text/plain": [
       "<Figure size 2500x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk', font_scale=0.9)\n",
    "expr_norm = (expr.loc[deletion_ids,:] - np.mean(expr.loc[deletion_ids,:].values,axis = 1, keepdims = True))/np.std(expr.loc[deletion_ids,:].values,axis = 1, keepdims = True)\n",
    "f,ax = plt.subplots(1,2, figsize = (25,20))\n",
    "\n",
    "sns.heatmap((expr.loc[deletion_ids,:]).T, yticklabels = 1, xticklabels = deletion_genes, ax = ax[0])\n",
    "sns.heatmap((expr_norm.loc[deletion_ids,:]).T, yticklabels = 1, xticklabels = deletion_genes, ax = ax[1])\n",
    "ticks = ax[0].get_yticks()\n",
    "ax[0].set_yticks(ax[0].get_yticks()[::11]);  \n",
    "ax[1].set_yticks(ax[1].get_yticks()[::11]); \n",
    "labels = [i.get_text().split('_')[0] for i in ax[0].get_yticklabels()]\n",
    " \n",
    "ax[0].set_yticklabels(labels)\n",
    "ax[1].set_yticklabels(labels)\n",
    "\n",
    "ax[0].set_title('Expression of deletion genes')\n",
    "ax[1].set_title('Normalized Expression of deletion genes')\n",
    "\n",
    "f.savefig(results_folder+'expression_of_deletion.pdf', bbox_inches='tight', format = 'pdf')\n",
    "#f.savefig('../results/results_20240211/expression_of_deletion.svg', bbox_inches='tight', format = 'svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's check the expression of the deletion genes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are gonna read only the edges that correspond to the engineered deletions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal81_Proline</th>\n",
       "      <th>rtg1_CStarve</th>\n",
       "      <th>gat1_AmmoniumSulfate</th>\n",
       "      <th>rtg1_YPDRapa</th>\n",
       "      <th>rtg1_MinimalEtOH</th>\n",
       "      <th>WT(ho)_MinimalGlucose</th>\n",
       "      <th>rtg1_YPD</th>\n",
       "      <th>dal80_YPEtOH</th>\n",
       "      <th>...</th>\n",
       "      <th>rtg3_YPDDiauxic</th>\n",
       "      <th>gcn4_YPDRapa</th>\n",
       "      <th>rtg3_CStarve</th>\n",
       "      <th>dal81_YPEtOH</th>\n",
       "      <th>rtg1_Glutamine</th>\n",
       "      <th>rtg3_MinimalEtOH</th>\n",
       "      <th>dal82_MinimalEtOH</th>\n",
       "      <th>dal80_Proline</th>\n",
       "      <th>stp1_Proline</th>\n",
       "      <th>dal80_YPD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YKR034W</td>\n",
       "      <td>0.139764</td>\n",
       "      <td>0.152932</td>\n",
       "      <td>0.154705</td>\n",
       "      <td>0.139958</td>\n",
       "      <td>0.108594</td>\n",
       "      <td>0.146124</td>\n",
       "      <td>0.154302</td>\n",
       "      <td>0.119653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138358</td>\n",
       "      <td>0.162247</td>\n",
       "      <td>0.122878</td>\n",
       "      <td>0.154688</td>\n",
       "      <td>0.155663</td>\n",
       "      <td>0.094390</td>\n",
       "      <td>0.107728</td>\n",
       "      <td>0.149095</td>\n",
       "      <td>0.143556</td>\n",
       "      <td>0.154524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YFL021W</td>\n",
       "      <td>0.014378</td>\n",
       "      <td>0.058058</td>\n",
       "      <td>0.070822</td>\n",
       "      <td>0.051392</td>\n",
       "      <td>-0.014635</td>\n",
       "      <td>0.056629</td>\n",
       "      <td>0.064416</td>\n",
       "      <td>0.025273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041101</td>\n",
       "      <td>0.054713</td>\n",
       "      <td>0.018156</td>\n",
       "      <td>0.057388</td>\n",
       "      <td>0.081564</td>\n",
       "      <td>-0.018606</td>\n",
       "      <td>-0.007675</td>\n",
       "      <td>0.017871</td>\n",
       "      <td>0.037686</td>\n",
       "      <td>0.068919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YER040W</td>\n",
       "      <td>0.019451</td>\n",
       "      <td>0.045159</td>\n",
       "      <td>0.029681</td>\n",
       "      <td>0.039332</td>\n",
       "      <td>-0.012040</td>\n",
       "      <td>0.025472</td>\n",
       "      <td>0.037328</td>\n",
       "      <td>0.050030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023844</td>\n",
       "      <td>0.030241</td>\n",
       "      <td>-0.008153</td>\n",
       "      <td>-0.008032</td>\n",
       "      <td>-0.008309</td>\n",
       "      <td>-0.011011</td>\n",
       "      <td>-0.020544</td>\n",
       "      <td>0.044726</td>\n",
       "      <td>0.012007</td>\n",
       "      <td>0.041727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YJL110C</td>\n",
       "      <td>-0.064842</td>\n",
       "      <td>-0.005767</td>\n",
       "      <td>-0.006496</td>\n",
       "      <td>-0.015110</td>\n",
       "      <td>-0.034203</td>\n",
       "      <td>-0.018849</td>\n",
       "      <td>-0.018696</td>\n",
       "      <td>-0.032170</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027463</td>\n",
       "      <td>-0.011749</td>\n",
       "      <td>-0.046840</td>\n",
       "      <td>-0.031984</td>\n",
       "      <td>-0.019023</td>\n",
       "      <td>-0.054349</td>\n",
       "      <td>-0.039076</td>\n",
       "      <td>-0.055077</td>\n",
       "      <td>-0.049924</td>\n",
       "      <td>-0.013747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YEL009C</td>\n",
       "      <td>0.076717</td>\n",
       "      <td>0.165228</td>\n",
       "      <td>0.116827</td>\n",
       "      <td>0.141906</td>\n",
       "      <td>0.095326</td>\n",
       "      <td>0.144194</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.159834</td>\n",
       "      <td>...</td>\n",
       "      <td>0.139414</td>\n",
       "      <td>0.081865</td>\n",
       "      <td>0.109161</td>\n",
       "      <td>0.122835</td>\n",
       "      <td>0.150102</td>\n",
       "      <td>0.102465</td>\n",
       "      <td>0.093638</td>\n",
       "      <td>0.148729</td>\n",
       "      <td>0.104848</td>\n",
       "      <td>0.153525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58675</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YEL009C</td>\n",
       "      <td>0.553271</td>\n",
       "      <td>0.574525</td>\n",
       "      <td>0.545349</td>\n",
       "      <td>0.539652</td>\n",
       "      <td>0.508049</td>\n",
       "      <td>0.545495</td>\n",
       "      <td>0.498871</td>\n",
       "      <td>0.558306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.528153</td>\n",
       "      <td>0.508667</td>\n",
       "      <td>0.572744</td>\n",
       "      <td>0.549448</td>\n",
       "      <td>0.542413</td>\n",
       "      <td>0.475141</td>\n",
       "      <td>0.534568</td>\n",
       "      <td>0.530501</td>\n",
       "      <td>0.550481</td>\n",
       "      <td>0.486900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58676</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YDR463W</td>\n",
       "      <td>-0.117161</td>\n",
       "      <td>-0.060704</td>\n",
       "      <td>-0.090189</td>\n",
       "      <td>-0.102476</td>\n",
       "      <td>-0.091227</td>\n",
       "      <td>-0.083396</td>\n",
       "      <td>-0.129024</td>\n",
       "      <td>-0.069745</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.088269</td>\n",
       "      <td>-0.081921</td>\n",
       "      <td>-0.056317</td>\n",
       "      <td>-0.057367</td>\n",
       "      <td>-0.063555</td>\n",
       "      <td>-0.161569</td>\n",
       "      <td>-0.072565</td>\n",
       "      <td>-0.165125</td>\n",
       "      <td>-0.173960</td>\n",
       "      <td>-0.127607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58677</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YHR006W</td>\n",
       "      <td>-0.058762</td>\n",
       "      <td>-0.010168</td>\n",
       "      <td>-0.052850</td>\n",
       "      <td>-0.009882</td>\n",
       "      <td>-0.042839</td>\n",
       "      <td>-0.010105</td>\n",
       "      <td>-0.090498</td>\n",
       "      <td>-0.025998</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043025</td>\n",
       "      <td>-0.037569</td>\n",
       "      <td>-0.002373</td>\n",
       "      <td>-0.064159</td>\n",
       "      <td>-0.013705</td>\n",
       "      <td>-0.091930</td>\n",
       "      <td>-0.008922</td>\n",
       "      <td>-0.015431</td>\n",
       "      <td>-0.056111</td>\n",
       "      <td>-0.083133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58678</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YOL067C</td>\n",
       "      <td>0.133502</td>\n",
       "      <td>0.136430</td>\n",
       "      <td>0.109979</td>\n",
       "      <td>0.097898</td>\n",
       "      <td>0.119384</td>\n",
       "      <td>0.150997</td>\n",
       "      <td>0.093785</td>\n",
       "      <td>0.168581</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106608</td>\n",
       "      <td>0.115936</td>\n",
       "      <td>0.138129</td>\n",
       "      <td>0.182199</td>\n",
       "      <td>0.108886</td>\n",
       "      <td>0.128941</td>\n",
       "      <td>0.111300</td>\n",
       "      <td>0.139926</td>\n",
       "      <td>0.028635</td>\n",
       "      <td>0.107695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58679</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YBL103C</td>\n",
       "      <td>0.046914</td>\n",
       "      <td>0.056471</td>\n",
       "      <td>-0.004644</td>\n",
       "      <td>0.040222</td>\n",
       "      <td>0.002216</td>\n",
       "      <td>-0.022425</td>\n",
       "      <td>-0.009684</td>\n",
       "      <td>0.044191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024801</td>\n",
       "      <td>0.012894</td>\n",
       "      <td>0.035945</td>\n",
       "      <td>-0.018136</td>\n",
       "      <td>0.028671</td>\n",
       "      <td>0.034532</td>\n",
       "      <td>0.034623</td>\n",
       "      <td>-0.159325</td>\n",
       "      <td>-0.080393</td>\n",
       "      <td>-0.006523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58680 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         gene1    gene2  dal81_Proline  rtg1_CStarve  gat1_AmmoniumSulfate  \\\n",
       "0      YDL248W  YKR034W       0.139764      0.152932              0.154705   \n",
       "1      YDL248W  YFL021W       0.014378      0.058058              0.070822   \n",
       "2      YDL248W  YER040W       0.019451      0.045159              0.029681   \n",
       "3      YDL248W  YJL110C      -0.064842     -0.005767             -0.006496   \n",
       "4      YDL248W  YEL009C       0.076717      0.165228              0.116827   \n",
       "...        ...      ...            ...           ...                   ...   \n",
       "58675    NATMX  YEL009C       0.553271      0.574525              0.545349   \n",
       "58676    NATMX  YDR463W      -0.117161     -0.060704             -0.090189   \n",
       "58677    NATMX  YHR006W      -0.058762     -0.010168             -0.052850   \n",
       "58678    NATMX  YOL067C       0.133502      0.136430              0.109979   \n",
       "58679    NATMX  YBL103C       0.046914      0.056471             -0.004644   \n",
       "\n",
       "       rtg1_YPDRapa  rtg1_MinimalEtOH  WT(ho)_MinimalGlucose  rtg1_YPD  \\\n",
       "0          0.139958          0.108594               0.146124  0.154302   \n",
       "1          0.051392         -0.014635               0.056629  0.064416   \n",
       "2          0.039332         -0.012040               0.025472  0.037328   \n",
       "3         -0.015110         -0.034203              -0.018849 -0.018696   \n",
       "4          0.141906          0.095326               0.144194  0.149533   \n",
       "...             ...               ...                    ...       ...   \n",
       "58675      0.539652          0.508049               0.545495  0.498871   \n",
       "58676     -0.102476         -0.091227              -0.083396 -0.129024   \n",
       "58677     -0.009882         -0.042839              -0.010105 -0.090498   \n",
       "58678      0.097898          0.119384               0.150997  0.093785   \n",
       "58679      0.040222          0.002216              -0.022425 -0.009684   \n",
       "\n",
       "       dal80_YPEtOH  ...  rtg3_YPDDiauxic  gcn4_YPDRapa  rtg3_CStarve  \\\n",
       "0          0.119653  ...         0.138358      0.162247      0.122878   \n",
       "1          0.025273  ...         0.041101      0.054713      0.018156   \n",
       "2          0.050030  ...         0.023844      0.030241     -0.008153   \n",
       "3         -0.032170  ...        -0.027463     -0.011749     -0.046840   \n",
       "4          0.159834  ...         0.139414      0.081865      0.109161   \n",
       "...             ...  ...              ...           ...           ...   \n",
       "58675      0.558306  ...         0.528153      0.508667      0.572744   \n",
       "58676     -0.069745  ...        -0.088269     -0.081921     -0.056317   \n",
       "58677     -0.025998  ...        -0.043025     -0.037569     -0.002373   \n",
       "58678      0.168581  ...         0.106608      0.115936      0.138129   \n",
       "58679      0.044191  ...         0.024801      0.012894      0.035945   \n",
       "\n",
       "       dal81_YPEtOH  rtg1_Glutamine  rtg3_MinimalEtOH  dal82_MinimalEtOH  \\\n",
       "0          0.154688        0.155663          0.094390           0.107728   \n",
       "1          0.057388        0.081564         -0.018606          -0.007675   \n",
       "2         -0.008032       -0.008309         -0.011011          -0.020544   \n",
       "3         -0.031984       -0.019023         -0.054349          -0.039076   \n",
       "4          0.122835        0.150102          0.102465           0.093638   \n",
       "...             ...             ...               ...                ...   \n",
       "58675      0.549448        0.542413          0.475141           0.534568   \n",
       "58676     -0.057367       -0.063555         -0.161569          -0.072565   \n",
       "58677     -0.064159       -0.013705         -0.091930          -0.008922   \n",
       "58678      0.182199        0.108886          0.128941           0.111300   \n",
       "58679     -0.018136        0.028671          0.034532           0.034623   \n",
       "\n",
       "       dal80_Proline  stp1_Proline  dal80_YPD  \n",
       "0           0.149095      0.143556   0.154524  \n",
       "1           0.017871      0.037686   0.068919  \n",
       "2           0.044726      0.012007   0.041727  \n",
       "3          -0.055077     -0.049924  -0.013747  \n",
       "4           0.148729      0.104848   0.153525  \n",
       "...              ...           ...        ...  \n",
       "58675       0.530501      0.550481   0.486900  \n",
       "58676      -0.165125     -0.173960  -0.127607  \n",
       "58677      -0.015431     -0.056111  -0.083133  \n",
       "58678       0.139926      0.028635   0.107695  \n",
       "58679      -0.159325     -0.080393  -0.006523  \n",
       "\n",
       "[58680 rows x 134 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bonobos = pd.read_csv('../../bonobo_paper_yeast//results/bonobo_20230908/bonobo_inferelator_sparse_20230908_deletedgenes.csv')\n",
    "df_bonobos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>mean_bonobo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YKR034W</td>\n",
       "      <td>0.138811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YFL021W</td>\n",
       "      <td>0.041057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YER040W</td>\n",
       "      <td>0.024405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YJL110C</td>\n",
       "      <td>-0.027768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YEL009C</td>\n",
       "      <td>0.140373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58675</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YEL009C</td>\n",
       "      <td>0.540038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58676</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YDR463W</td>\n",
       "      <td>-0.088083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58677</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YHR006W</td>\n",
       "      <td>-0.036745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58678</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YOL067C</td>\n",
       "      <td>0.118547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58679</th>\n",
       "      <td>NATMX</td>\n",
       "      <td>YBL103C</td>\n",
       "      <td>0.010050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58680 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         gene1    gene2  mean_bonobo\n",
       "0      YDL248W  YKR034W     0.138811\n",
       "1      YDL248W  YFL021W     0.041057\n",
       "2      YDL248W  YER040W     0.024405\n",
       "3      YDL248W  YJL110C    -0.027768\n",
       "4      YDL248W  YEL009C     0.140373\n",
       "...        ...      ...          ...\n",
       "58675    NATMX  YEL009C     0.540038\n",
       "58676    NATMX  YDR463W    -0.088083\n",
       "58677    NATMX  YHR006W    -0.036745\n",
       "58678    NATMX  YOL067C     0.118547\n",
       "58679    NATMX  YBL103C     0.010050\n",
       "\n",
       "[58680 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We need the average of the bonobos\n",
    "mean_bonobo = np.mean(df_bonobos.iloc[:,2:].values, axis = 1)\n",
    "# We put it in a dataframe\n",
    "df_mean_bonobo = df_bonobos.iloc[:,:2]\n",
    "df_mean_bonobo['mean_bonobo'] = mean_bonobo\n",
    "df_mean_bonobo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's see what the heatmap is for gcn4 (YEL009C)\n",
    "# gene_maps = {'dal80':'YKR034W','gat1':'YFL021W', 'gln3':'YER040W', 'gzf3':'YJL110C', 'gcn4':'YEL009C', 'stp1':'YDR463W', 'stp2':'YHR006W', 'rtg1':'YOL067C', 'rtg3':'YBL103C' }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_columns_by_genotype = sorted(df_bonobos.columns[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select variable genes\n",
    "for i in ['gcn4']:#deletion_genes:\n",
    "    geneoi = i\n",
    "    temp = df_bonobos[df_bonobos['gene2']==gene_maps[geneoi]]\n",
    "    temp.index = temp['gene1']\n",
    "    temp = temp.iloc[:,2:]\n",
    "\n",
    "    # Calculate the row-wise variance\n",
    "    row_variances = temp.mean(axis=1) #,mean\n",
    "\n",
    "    # Add the row_variances as a new column to the DataFrame\n",
    "    temp['row_variance'] = row_variances\n",
    "\n",
    "    # Sort the DataFrame based on the Row_Variance column\n",
    "    sorted_temp = temp.sort_values(by='row_variance', ascending=False)\n",
    "\n",
    "    g1 = sns.clustermap(pd.concat([sorted_temp.iloc[:50,:-1].loc[:,sort_columns_by_genotype],sorted_temp.iloc[-50:,:-1].loc[:,sort_columns_by_genotype]], axis =0) ,\n",
    "                        col_cluster=False,row_cluster = False, cmap = 'jet',\n",
    "                        xticklabels = 1,yticklabels =False, figsize = (20,10), method = 'complete', metric = 'sqeuclidean')\n",
    "    #g1 = sns.clustermap(sorted_temp.iloc[:50,:-1].loc[:,sort_columns_by_genotype], col_cluster=False,row_cluster = False, cmap = 'jet', xticklabels = 1, yticklabels = 1, figsize = (20,10), method = 'complete', metric = 'sqeuclidean')\n",
    "\n",
    "    g1.ax_heatmap.set_ylabel(\"GCN4-target edges\")\n",
    "    g1.ax_heatmap.set_title('Top and bottom 50 %s edges, sorted by average edge values ' %geneoi)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    #g1.fig.savefig('../results/results_20240211/strong_edges'+geneoi+'.pdf', bbox_inches='tight', format = 'pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select variable genes\n",
    "for i in ['gcn4']:\n",
    "    geneoi = i\n",
    "    temp = df_bonobos[df_bonobos['gene2']==gene_maps[geneoi]]\n",
    "    temp.index = temp['gene1']\n",
    "    temp = temp.iloc[:,2:]\n",
    "\n",
    "    # Calculate the row-wise variance\n",
    "    row_variances = temp.var(axis=1) #,mean\n",
    "\n",
    "    # Add the row_variances as a new column to the DataFrame\n",
    "    temp['row_variance'] = row_variances\n",
    "\n",
    "    # Sort the DataFrame based on the Row_Variance column\n",
    "    sorted_temp = temp.sort_values(by='row_variance', ascending=False)\n",
    "\n",
    "    g1 = sns.clustermap(sorted_temp.iloc[:100,:-1].loc[:,sort_columns_by_genotype], col_cluster=False,row_cluster = False, \n",
    "                        cmap = 'jet', xticklabels = 1, yticklabels = False, figsize = (20,10), method = 'complete', metric = 'sqeuclidean')\n",
    "\n",
    "    g1.ax_heatmap.set_ylabel(\"GCN4-target edges\")\n",
    "    g1.ax_heatmap.set_title('Most variable %s edges, sorted by variance ' %geneoi)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    #g1.fig.savefig('../results/results_20240211/variable_edges'+geneoi+'.pdf', bbox_inches='tight', format = 'pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GCN4 edges that are the most affected by GCN4 deletion. \n",
    "We compute the average expression by genotype and then we normalize the scores. This way for each gcn4-X edge we know\n",
    "how different the value for each KO is from the overall average.\n",
    "\n",
    "We can then select the edges with the highest and lowest values for the GCN4 deletion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gene1</th>\n",
       "      <th>YDL248W</th>\n",
       "      <th>YDL247W</th>\n",
       "      <th>YDL246C</th>\n",
       "      <th>YDL245C</th>\n",
       "      <th>YDL244W</th>\n",
       "      <th>YDL243C</th>\n",
       "      <th>YDL242W</th>\n",
       "      <th>YDL241W</th>\n",
       "      <th>YDL240W</th>\n",
       "      <th>YDL239C</th>\n",
       "      <th>...</th>\n",
       "      <th>Q0130</th>\n",
       "      <th>Q0140</th>\n",
       "      <th>X21S_rRNA</th>\n",
       "      <th>Q0160</th>\n",
       "      <th>Q0250</th>\n",
       "      <th>Q0255</th>\n",
       "      <th>Q0275</th>\n",
       "      <th>RPM1</th>\n",
       "      <th>KANMX</th>\n",
       "      <th>NATMX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genotype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WT(ho)</th>\n",
       "      <td>-1.596631</td>\n",
       "      <td>-0.315899</td>\n",
       "      <td>-0.240718</td>\n",
       "      <td>-1.990027</td>\n",
       "      <td>-0.853817</td>\n",
       "      <td>-0.984680</td>\n",
       "      <td>-0.618114</td>\n",
       "      <td>0.046161</td>\n",
       "      <td>0.368420</td>\n",
       "      <td>-0.312513</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.061403</td>\n",
       "      <td>-0.344898</td>\n",
       "      <td>0.261241</td>\n",
       "      <td>0.316151</td>\n",
       "      <td>0.873833</td>\n",
       "      <td>-0.251833</td>\n",
       "      <td>0.578546</td>\n",
       "      <td>-0.101436</td>\n",
       "      <td>-0.452240</td>\n",
       "      <td>-0.263167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal80</th>\n",
       "      <td>-0.242421</td>\n",
       "      <td>-0.508267</td>\n",
       "      <td>-0.437990</td>\n",
       "      <td>-0.468471</td>\n",
       "      <td>0.777197</td>\n",
       "      <td>0.640297</td>\n",
       "      <td>-1.020771</td>\n",
       "      <td>0.715037</td>\n",
       "      <td>-0.370629</td>\n",
       "      <td>0.896256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.568676</td>\n",
       "      <td>-0.486964</td>\n",
       "      <td>0.534266</td>\n",
       "      <td>0.536696</td>\n",
       "      <td>-0.986069</td>\n",
       "      <td>1.318138</td>\n",
       "      <td>-0.057351</td>\n",
       "      <td>-0.235479</td>\n",
       "      <td>-1.431910</td>\n",
       "      <td>-0.647759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal81</th>\n",
       "      <td>1.190837</td>\n",
       "      <td>-0.687383</td>\n",
       "      <td>-0.612457</td>\n",
       "      <td>-0.264706</td>\n",
       "      <td>0.364109</td>\n",
       "      <td>-0.161710</td>\n",
       "      <td>-0.230097</td>\n",
       "      <td>2.211591</td>\n",
       "      <td>0.290103</td>\n",
       "      <td>0.531433</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270558</td>\n",
       "      <td>-0.670247</td>\n",
       "      <td>0.192851</td>\n",
       "      <td>0.113379</td>\n",
       "      <td>-0.244135</td>\n",
       "      <td>-0.554212</td>\n",
       "      <td>0.069605</td>\n",
       "      <td>-0.321838</td>\n",
       "      <td>-0.915540</td>\n",
       "      <td>-0.319121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal82</th>\n",
       "      <td>-1.284514</td>\n",
       "      <td>-0.357465</td>\n",
       "      <td>-0.278388</td>\n",
       "      <td>-0.326043</td>\n",
       "      <td>0.853736</td>\n",
       "      <td>0.554005</td>\n",
       "      <td>-0.078383</td>\n",
       "      <td>-0.551429</td>\n",
       "      <td>1.789958</td>\n",
       "      <td>1.392828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692862</td>\n",
       "      <td>-0.403023</td>\n",
       "      <td>0.712232</td>\n",
       "      <td>0.405908</td>\n",
       "      <td>-0.841305</td>\n",
       "      <td>2.445865</td>\n",
       "      <td>-0.404915</td>\n",
       "      <td>-0.109725</td>\n",
       "      <td>0.353823</td>\n",
       "      <td>-0.047445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gat1</th>\n",
       "      <td>0.653914</td>\n",
       "      <td>-0.617513</td>\n",
       "      <td>-0.545778</td>\n",
       "      <td>-0.223950</td>\n",
       "      <td>-0.399025</td>\n",
       "      <td>-1.223790</td>\n",
       "      <td>-0.712630</td>\n",
       "      <td>-0.139953</td>\n",
       "      <td>-0.074993</td>\n",
       "      <td>-1.505001</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.874786</td>\n",
       "      <td>-0.591131</td>\n",
       "      <td>-0.640859</td>\n",
       "      <td>-0.882217</td>\n",
       "      <td>-0.128083</td>\n",
       "      <td>-0.520026</td>\n",
       "      <td>-0.751013</td>\n",
       "      <td>-0.293894</td>\n",
       "      <td>0.206621</td>\n",
       "      <td>-0.196317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gcn4</th>\n",
       "      <td>-0.592250</td>\n",
       "      <td>2.263220</td>\n",
       "      <td>2.676162</td>\n",
       "      <td>1.495480</td>\n",
       "      <td>1.965703</td>\n",
       "      <td>-2.088784</td>\n",
       "      <td>-0.270473</td>\n",
       "      <td>-1.589399</td>\n",
       "      <td>-1.321291</td>\n",
       "      <td>-1.729990</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.950677</td>\n",
       "      <td>0.054375</td>\n",
       "      <td>-3.117765</td>\n",
       "      <td>-2.537757</td>\n",
       "      <td>2.892943</td>\n",
       "      <td>-0.355929</td>\n",
       "      <td>1.587643</td>\n",
       "      <td>2.830524</td>\n",
       "      <td>2.323339</td>\n",
       "      <td>3.248336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gln3</th>\n",
       "      <td>1.868969</td>\n",
       "      <td>0.164625</td>\n",
       "      <td>0.267208</td>\n",
       "      <td>0.237778</td>\n",
       "      <td>-1.179767</td>\n",
       "      <td>1.004669</td>\n",
       "      <td>2.303790</td>\n",
       "      <td>-0.363466</td>\n",
       "      <td>-1.457290</td>\n",
       "      <td>0.375047</td>\n",
       "      <td>...</td>\n",
       "      <td>0.971767</td>\n",
       "      <td>-0.073883</td>\n",
       "      <td>0.745519</td>\n",
       "      <td>0.156266</td>\n",
       "      <td>-0.758390</td>\n",
       "      <td>-1.653630</td>\n",
       "      <td>-1.047122</td>\n",
       "      <td>0.296600</td>\n",
       "      <td>1.376634</td>\n",
       "      <td>-0.098821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gzf3</th>\n",
       "      <td>-0.598621</td>\n",
       "      <td>-0.381820</td>\n",
       "      <td>-0.304161</td>\n",
       "      <td>-0.085089</td>\n",
       "      <td>0.971893</td>\n",
       "      <td>1.170331</td>\n",
       "      <td>-0.230178</td>\n",
       "      <td>-0.942008</td>\n",
       "      <td>-1.600672</td>\n",
       "      <td>0.733195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.343213</td>\n",
       "      <td>0.172336</td>\n",
       "      <td>0.469250</td>\n",
       "      <td>1.501648</td>\n",
       "      <td>-0.102987</td>\n",
       "      <td>0.507945</td>\n",
       "      <td>2.362272</td>\n",
       "      <td>-1.114769</td>\n",
       "      <td>-0.301667</td>\n",
       "      <td>-0.423552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rtg1</th>\n",
       "      <td>-0.514327</td>\n",
       "      <td>-0.657615</td>\n",
       "      <td>-0.583827</td>\n",
       "      <td>-0.081855</td>\n",
       "      <td>0.294905</td>\n",
       "      <td>-0.853195</td>\n",
       "      <td>-0.407839</td>\n",
       "      <td>-0.638084</td>\n",
       "      <td>0.297841</td>\n",
       "      <td>0.846286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155119</td>\n",
       "      <td>-0.691907</td>\n",
       "      <td>0.236072</td>\n",
       "      <td>0.448242</td>\n",
       "      <td>-0.187350</td>\n",
       "      <td>-0.538097</td>\n",
       "      <td>-0.168582</td>\n",
       "      <td>-1.296073</td>\n",
       "      <td>-0.016556</td>\n",
       "      <td>-0.178236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rtg3</th>\n",
       "      <td>0.688605</td>\n",
       "      <td>2.103029</td>\n",
       "      <td>-0.945846</td>\n",
       "      <td>-0.375084</td>\n",
       "      <td>-1.253764</td>\n",
       "      <td>0.842903</td>\n",
       "      <td>-0.086131</td>\n",
       "      <td>-0.653018</td>\n",
       "      <td>0.951735</td>\n",
       "      <td>-1.456948</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125334</td>\n",
       "      <td>3.098759</td>\n",
       "      <td>0.109341</td>\n",
       "      <td>-0.991794</td>\n",
       "      <td>0.059371</td>\n",
       "      <td>-0.061242</td>\n",
       "      <td>-1.023068</td>\n",
       "      <td>0.782610</td>\n",
       "      <td>-1.057261</td>\n",
       "      <td>-0.700763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stp1</th>\n",
       "      <td>0.870692</td>\n",
       "      <td>-0.451938</td>\n",
       "      <td>1.485927</td>\n",
       "      <td>-0.179444</td>\n",
       "      <td>-1.318031</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>1.996271</td>\n",
       "      <td>0.753732</td>\n",
       "      <td>0.132048</td>\n",
       "      <td>-0.079372</td>\n",
       "      <td>...</td>\n",
       "      <td>0.638942</td>\n",
       "      <td>-0.561638</td>\n",
       "      <td>0.288184</td>\n",
       "      <td>0.069093</td>\n",
       "      <td>-0.688809</td>\n",
       "      <td>0.123058</td>\n",
       "      <td>-0.574658</td>\n",
       "      <td>-0.185358</td>\n",
       "      <td>-0.384145</td>\n",
       "      <td>-0.319268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stp2</th>\n",
       "      <td>-0.444253</td>\n",
       "      <td>-0.552976</td>\n",
       "      <td>-0.480132</td>\n",
       "      <td>2.261410</td>\n",
       "      <td>-0.223140</td>\n",
       "      <td>0.596127</td>\n",
       "      <td>-0.645444</td>\n",
       "      <td>1.150837</td>\n",
       "      <td>0.994770</td>\n",
       "      <td>0.308778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.371064</td>\n",
       "      <td>0.498221</td>\n",
       "      <td>0.209667</td>\n",
       "      <td>0.864385</td>\n",
       "      <td>0.110980</td>\n",
       "      <td>-0.460038</td>\n",
       "      <td>-0.571356</td>\n",
       "      <td>-0.251163</td>\n",
       "      <td>0.298902</td>\n",
       "      <td>-0.053885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 6520 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gene1      YDL248W   YDL247W   YDL246C   YDL245C   YDL244W   YDL243C  \\\n",
       "genotype                                                               \n",
       "WT(ho)   -1.596631 -0.315899 -0.240718 -1.990027 -0.853817 -0.984680   \n",
       "dal80    -0.242421 -0.508267 -0.437990 -0.468471  0.777197  0.640297   \n",
       "dal81     1.190837 -0.687383 -0.612457 -0.264706  0.364109 -0.161710   \n",
       "dal82    -1.284514 -0.357465 -0.278388 -0.326043  0.853736  0.554005   \n",
       "gat1      0.653914 -0.617513 -0.545778 -0.223950 -0.399025 -1.223790   \n",
       "gcn4     -0.592250  2.263220  2.676162  1.495480  1.965703 -2.088784   \n",
       "gln3      1.868969  0.164625  0.267208  0.237778 -1.179767  1.004669   \n",
       "gzf3     -0.598621 -0.381820 -0.304161 -0.085089  0.971893  1.170331   \n",
       "rtg1     -0.514327 -0.657615 -0.583827 -0.081855  0.294905 -0.853195   \n",
       "rtg3      0.688605  2.103029 -0.945846 -0.375084 -1.253764  0.842903   \n",
       "stp1      0.870692 -0.451938  1.485927 -0.179444 -1.318031  0.503826   \n",
       "stp2     -0.444253 -0.552976 -0.480132  2.261410 -0.223140  0.596127   \n",
       "\n",
       "gene1      YDL242W   YDL241W   YDL240W   YDL239C  ...     Q0130     Q0140  \\\n",
       "genotype                                          ...                       \n",
       "WT(ho)   -0.618114  0.046161  0.368420 -0.312513  ... -0.061403 -0.344898   \n",
       "dal80    -1.020771  0.715037 -0.370629  0.896256  ...  0.568676 -0.486964   \n",
       "dal81    -0.230097  2.211591  0.290103  0.531433  ...  0.270558 -0.670247   \n",
       "dal82    -0.078383 -0.551429  1.789958  1.392828  ...  0.692862 -0.403023   \n",
       "gat1     -0.712630 -0.139953 -0.074993 -1.505001  ... -0.874786 -0.591131   \n",
       "gcn4     -0.270473 -1.589399 -1.321291 -1.729990  ... -2.950677  0.054375   \n",
       "gln3      2.303790 -0.363466 -1.457290  0.375047  ...  0.971767 -0.073883   \n",
       "gzf3     -0.230178 -0.942008 -1.600672  0.733195  ...  0.343213  0.172336   \n",
       "rtg1     -0.407839 -0.638084  0.297841  0.846286  ...  0.155119 -0.691907   \n",
       "rtg3     -0.086131 -0.653018  0.951735 -1.456948  ... -0.125334  3.098759   \n",
       "stp1      1.996271  0.753732  0.132048 -0.079372  ...  0.638942 -0.561638   \n",
       "stp2     -0.645444  1.150837  0.994770  0.308778  ...  0.371064  0.498221   \n",
       "\n",
       "gene1     X21S_rRNA     Q0160     Q0250     Q0255     Q0275      RPM1  \\\n",
       "genotype                                                                \n",
       "WT(ho)     0.261241  0.316151  0.873833 -0.251833  0.578546 -0.101436   \n",
       "dal80      0.534266  0.536696 -0.986069  1.318138 -0.057351 -0.235479   \n",
       "dal81      0.192851  0.113379 -0.244135 -0.554212  0.069605 -0.321838   \n",
       "dal82      0.712232  0.405908 -0.841305  2.445865 -0.404915 -0.109725   \n",
       "gat1      -0.640859 -0.882217 -0.128083 -0.520026 -0.751013 -0.293894   \n",
       "gcn4      -3.117765 -2.537757  2.892943 -0.355929  1.587643  2.830524   \n",
       "gln3       0.745519  0.156266 -0.758390 -1.653630 -1.047122  0.296600   \n",
       "gzf3       0.469250  1.501648 -0.102987  0.507945  2.362272 -1.114769   \n",
       "rtg1       0.236072  0.448242 -0.187350 -0.538097 -0.168582 -1.296073   \n",
       "rtg3       0.109341 -0.991794  0.059371 -0.061242 -1.023068  0.782610   \n",
       "stp1       0.288184  0.069093 -0.688809  0.123058 -0.574658 -0.185358   \n",
       "stp2       0.209667  0.864385  0.110980 -0.460038 -0.571356 -0.251163   \n",
       "\n",
       "gene1        KANMX     NATMX  \n",
       "genotype                      \n",
       "WT(ho)   -0.452240 -0.263167  \n",
       "dal80    -1.431910 -0.647759  \n",
       "dal81    -0.915540 -0.319121  \n",
       "dal82     0.353823 -0.047445  \n",
       "gat1      0.206621 -0.196317  \n",
       "gcn4      2.323339  3.248336  \n",
       "gln3      1.376634 -0.098821  \n",
       "gzf3     -0.301667 -0.423552  \n",
       "rtg1     -0.016556 -0.178236  \n",
       "rtg3     -1.057261 -0.700763  \n",
       "stp1     -0.384145 -0.319268  \n",
       "stp2      0.298902 -0.053885  \n",
       "\n",
       "[12 rows x 6520 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We need to ignore the variance/mean\n",
    "ttt = temp.T.iloc[:-1,:]\n",
    "# We need to keep track of the genotype\n",
    "ttt['genotype'] = [i.split('_')[0] for i in ttt.index]\n",
    "# We group by genotype and calculate the mean\n",
    "ttt_mean = ttt.groupby('genotype').mean()\n",
    "# Compute mean for each condition. This should allow to see which genes are the most variable for GCN4\n",
    "ttt_mean_norm = (ttt_mean - np.mean(ttt_mean.values,axis = 0))/np.std(ttt_mean.values,axis = 0)\n",
    "ttt_mean_norm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6520,)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can select the genes with highest and lowest values for GCN4 deletion\n",
    "ttt_mean_norm_gcn4 = ttt_mean_norm.iloc[:,((ttt_mean_norm.loc['gcn4',:].values>2.5)|(ttt_mean_norm.loc['gcn4',:].values<-3))]\n",
    "all_ttt_mean_norm_gcn4 = ttt_mean_norm.loc['gcn4',:].sort_values()\n",
    "all_ttt_mean_norm_gcn4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "ParseError",
     "evalue": "mismatched tag: line 62, column 2 (<string>)",
     "output_type": "error",
     "traceback": [
      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
      "\u001b[0m  File \u001b[1;32m/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3505\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n    exec(code_obj, self.user_global_ns, self.user_ns)\u001b[0m\n",
      "\u001b[0m  Cell \u001b[1;32mIn[52], line 1\u001b[0m\n    get_gene_yeast_gene_name(all_ttt_mean_norm_gcn4.iloc[:2].index)\u001b[0m\n",
      "\u001b[0m  Cell \u001b[1;32mIn[2], line 15\u001b[0m in \u001b[1;35mget_gene_yeast_gene_name\u001b[0m\n    bm = Biomart()\u001b[0m\n",
      "\u001b[0m  File \u001b[1;32m/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/gseapy/biomart.py:76\u001b[0m in \u001b[1;35m__init__\u001b[0m\n    self._marts = self.get_marts()[\"Mart\"].to_list()\u001b[0m\n",
      "\u001b[0m  File \u001b[1;32m/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/gseapy/biomart.py:157\u001b[0m in \u001b[1;35mget_marts\u001b[0m\n    marts = [e.attrib for e in ET.XML(resp.text)]\u001b[0m\n",
      "\u001b[0;36m  File \u001b[0;32m/opt/conda/envs/bonobo-env/lib/python3.9/xml/etree/ElementTree.py:1342\u001b[0;36m in \u001b[0;35mXML\u001b[0;36m\n\u001b[0;31m    parser.feed(text)\u001b[0;36m\n",
      "\u001b[0;36m  File \u001b[0;32m<string>\u001b[0;36m\u001b[0m\n\u001b[0;31mParseError\u001b[0m\u001b[0;31m:\u001b[0m mismatched tag: line 62, column 2\n"
     ]
    }
   ],
   "source": [
    "get_gene_yeast_gene_name(all_ttt_mean_norm_gcn4.iloc[:2].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cellular_Component_AutoRIF',\n",
       " 'Cellular_Component_AutoRIF_Predicted_zscore',\n",
       " 'GO_Biological_Process_2018',\n",
       " 'GO_Biological_Process_AutoRIF',\n",
       " 'GO_Biological_Process_AutoRIF_Predicted_zscore',\n",
       " 'GO_Cellular_Component_2018',\n",
       " 'GO_Cellular_Component_AutoRIF',\n",
       " 'GO_Cellular_Component_AutoRIF_Predicted_zscore',\n",
       " 'GO_Molecular_Function_2018',\n",
       " 'GO_Molecular_Function_AutoRIF',\n",
       " 'GO_Molecular_Function_AutoRIF_Predicted_zscore',\n",
       " 'Gene_Interaction_Hubs_BioGRID_2018',\n",
       " 'InterPro_Domains_2019',\n",
       " 'KEGG_2018',\n",
       " 'KEGG_2019',\n",
       " 'PPI_Hubs_BioGRID_2018',\n",
       " 'Pfam_Domains_2019',\n",
       " 'Phenotype_AutoRIF',\n",
       " 'Phenotype_AutoRIF_Predicted_zscore',\n",
       " 'TF2DNA_2018',\n",
       " 'WikiPathways_2018']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['Cellular_Component_AutoRIF',\n",
    " 'Cellular_Component_AutoRIF_Predicted_zscore',\n",
    " 'GO_Biological_Process_2018',\n",
    " 'GO_Biological_Process_AutoRIF',\n",
    " 'GO_Biological_Process_AutoRIF_Predicted_zscore',\n",
    " 'GO_Cellular_Component_2018',\n",
    " 'GO_Cellular_Component_AutoRIF',\n",
    " 'GO_Cellular_Component_AutoRIF_Predicted_zscore',\n",
    " 'GO_Molecular_Function_2018',\n",
    " 'GO_Molecular_Function_AutoRIF',\n",
    " 'GO_Molecular_Function_AutoRIF_Predicted_zscore',\n",
    " 'Gene_Interaction_Hubs_BioGRID_2018',\n",
    " 'InterPro_Domains_2019',\n",
    " 'KEGG_2018',\n",
    " 'KEGG_2019',\n",
    " 'PPI_Hubs_BioGRID_2018',\n",
    " 'Pfam_Domains_2019',\n",
    " 'Phenotype_AutoRIF',\n",
    " 'Phenotype_AutoRIF_Predicted_zscore',\n",
    " 'TF2DNA_2018',\n",
    " 'WikiPathways_2018']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/gseapy/plot.py:689: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.\n",
      "  df[self.colname].replace(\n",
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/gseapy/plot.py:689: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[self.colname].replace(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>Overlap</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Old P-value</th>\n",
       "      <th>Old Adjusted P-value</th>\n",
       "      <th>Z-score</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Exosome  sce04147</td>\n",
       "      <td>7/198</td>\n",
       "      <td>8.788324e-07</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.001513</td>\n",
       "      <td>0.072619</td>\n",
       "      <td>-1.796607</td>\n",
       "      <td>25.053093</td>\n",
       "      <td>ACT1;YPT7;SSA3;TEF2;PPH21;YOP1;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Transporters  sce02000</td>\n",
       "      <td>6/223</td>\n",
       "      <td>2.662950e-05</td>\n",
       "      <td>0.000639</td>\n",
       "      <td>0.012430</td>\n",
       "      <td>0.198877</td>\n",
       "      <td>-1.495411</td>\n",
       "      <td>15.751902</td>\n",
       "      <td>TIM21;ENB1;ITR2;ERV14;VCX1;FET4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Sphingolipid metabolism  sce00600</td>\n",
       "      <td>2/15</td>\n",
       "      <td>7.076742e-04</td>\n",
       "      <td>0.008492</td>\n",
       "      <td>0.009147</td>\n",
       "      <td>0.198877</td>\n",
       "      <td>-1.915461</td>\n",
       "      <td>13.893845</td>\n",
       "      <td>LAC1;LAG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Membrane trafficking  sce04131</td>\n",
       "      <td>6/386</td>\n",
       "      <td>5.307089e-04</td>\n",
       "      <td>0.008491</td>\n",
       "      <td>0.117026</td>\n",
       "      <td>0.513072</td>\n",
       "      <td>-1.534199</td>\n",
       "      <td>11.569851</td>\n",
       "      <td>YPT7;SSA3;YIP1;TEF2;RHO1;ERV14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Alanine, aspartate and glutamate metabolism  s...</td>\n",
       "      <td>2/33</td>\n",
       "      <td>3.451648e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.036325</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.848719</td>\n",
       "      <td>10.480207</td>\n",
       "      <td>CPA1;GFA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Endocytosis  sce04144</td>\n",
       "      <td>3/75</td>\n",
       "      <td>1.036996e-03</td>\n",
       "      <td>0.009955</td>\n",
       "      <td>0.029072</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.371145</td>\n",
       "      <td>9.421723</td>\n",
       "      <td>YPT7;SSA3;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Various types of N-glycan biosynthesis  sce00513</td>\n",
       "      <td>2/31</td>\n",
       "      <td>3.050112e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.032582</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.542810</td>\n",
       "      <td>8.936845</td>\n",
       "      <td>ALG11;MNN10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Phagosome  sce04145</td>\n",
       "      <td>2/35</td>\n",
       "      <td>3.876500e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.040227</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.587780</td>\n",
       "      <td>8.816661</td>\n",
       "      <td>ACT1;YPT7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>GTP-binding proteins  sce04031</td>\n",
       "      <td>2/36</td>\n",
       "      <td>4.097591e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.042235</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.506383</td>\n",
       "      <td>8.281124</td>\n",
       "      <td>YPT7;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ether lipid metabolism  sce00565</td>\n",
       "      <td>1/8</td>\n",
       "      <td>2.100805e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.075514</td>\n",
       "      <td>0.402742</td>\n",
       "      <td>-1.824594</td>\n",
       "      <td>7.048131</td>\n",
       "      <td>CPT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Translation factors  sce03012</td>\n",
       "      <td>2/70</td>\n",
       "      <td>1.483219e-02</td>\n",
       "      <td>0.071195</td>\n",
       "      <td>0.128268</td>\n",
       "      <td>0.513072</td>\n",
       "      <td>-1.659375</td>\n",
       "      <td>6.987554</td>\n",
       "      <td>HYP2;TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Protein phosphatases and associated proteins  ...</td>\n",
       "      <td>2/89</td>\n",
       "      <td>2.329615e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.186124</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.445695</td>\n",
       "      <td>5.435042</td>\n",
       "      <td>SSA3;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Autophagy - yeast  sce04138</td>\n",
       "      <td>2/86</td>\n",
       "      <td>2.185304e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.176717</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.167885</td>\n",
       "      <td>4.465310</td>\n",
       "      <td>YPT7;PPH21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gene_set                                               Term Overlap  \\\n",
       "0   KEGG_2018                                  Exosome  sce04147   7/198   \n",
       "1   KEGG_2018                             Transporters  sce02000   6/223   \n",
       "2   KEGG_2018                  Sphingolipid metabolism  sce00600    2/15   \n",
       "3   KEGG_2018                     Membrane trafficking  sce04131   6/386   \n",
       "4   KEGG_2018  Alanine, aspartate and glutamate metabolism  s...    2/33   \n",
       "5   KEGG_2018                              Endocytosis  sce04144    3/75   \n",
       "6   KEGG_2018   Various types of N-glycan biosynthesis  sce00513    2/31   \n",
       "7   KEGG_2018                                Phagosome  sce04145    2/35   \n",
       "8   KEGG_2018                     GTP-binding proteins  sce04031    2/36   \n",
       "9   KEGG_2018                   Ether lipid metabolism  sce00565     1/8   \n",
       "10  KEGG_2018                      Translation factors  sce03012    2/70   \n",
       "11  KEGG_2018  Protein phosphatases and associated proteins  ...    2/89   \n",
       "14  KEGG_2018                        Autophagy - yeast  sce04138    2/86   \n",
       "\n",
       "         P-value  Adjusted P-value  Old P-value  Old Adjusted P-value  \\\n",
       "0   8.788324e-07          0.000042     0.001513              0.072619   \n",
       "1   2.662950e-05          0.000639     0.012430              0.198877   \n",
       "2   7.076742e-04          0.008492     0.009147              0.198877   \n",
       "3   5.307089e-04          0.008491     0.117026              0.513072   \n",
       "4   3.451648e-03          0.021854     0.036325              0.253413   \n",
       "5   1.036996e-03          0.009955     0.029072              0.253413   \n",
       "6   3.050112e-03          0.021854     0.032582              0.253413   \n",
       "7   3.876500e-03          0.021854     0.040227              0.253413   \n",
       "8   4.097591e-03          0.021854     0.042235              0.253413   \n",
       "9   2.100805e-02          0.086017     0.075514              0.402742   \n",
       "10  1.483219e-02          0.071195     0.128268              0.513072   \n",
       "11  2.329615e-02          0.086017     0.186124              0.554377   \n",
       "14  2.185304e-02          0.086017     0.176717              0.554377   \n",
       "\n",
       "     Z-score  Combined Score                                Genes  \n",
       "0  -1.796607       25.053093  ACT1;YPT7;SSA3;TEF2;PPH21;YOP1;RHO1  \n",
       "1  -1.495411       15.751902      TIM21;ENB1;ITR2;ERV14;VCX1;FET4  \n",
       "2  -1.915461       13.893845                            LAC1;LAG1  \n",
       "3  -1.534199       11.569851       YPT7;SSA3;YIP1;TEF2;RHO1;ERV14  \n",
       "4  -1.848719       10.480207                            CPA1;GFA1  \n",
       "5  -1.371145        9.421723                       YPT7;SSA3;RHO1  \n",
       "6  -1.542810        8.936845                          ALG11;MNN10  \n",
       "7  -1.587780        8.816661                            ACT1;YPT7  \n",
       "8  -1.506383        8.281124                            YPT7;RHO1  \n",
       "9  -1.824594        7.048131                                 CPT1  \n",
       "10 -1.659375        6.987554                            HYP2;TEF2  \n",
       "11 -1.445695        5.435042                           SSA3;PPH21  \n",
       "14 -1.167885        4.465310                           YPT7;PPH21  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gseapy as gp\n",
    "from gseapy import Biomart\n",
    "# yeast\n",
    "yeast = gp.get_library_name(organism='Yeast')\n",
    "\n",
    "df_paths = pd.DataFrame()\n",
    "\n",
    "gene_list = all_ttt_mean_norm_gcn4.index.tolist()[:50] + all_ttt_mean_norm_gcn4.index.tolist()[-50:-1] # We don't want to use the GCN4 gene\n",
    "\n",
    "gene_list_gene_name = get_gene_yeast_gene_name(gene_list)\n",
    "\n",
    "# if you are only intrested in dataframe that enrichr returned, please set outdir=None\n",
    "enr = gp.enrichr(gene_list=gene_list_gene_name, # or \"./tests/data/gene_list.txt\",\n",
    "                    gene_sets=['KEGG_2018'],\n",
    "                    organism='yeast', # don't forget to set organism to the one you desired! e.g. Yeast\n",
    "                    outdir='../results/pseudobulk_nonzero/', # don't write to disk\n",
    "                    )\n",
    "\n",
    "# obj.results stores all results\n",
    "enr.results.head(5)\n",
    "    \n",
    "btemp = enr.results.copy()\n",
    "btemp[btemp['Adjusted P-value']<0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>Overlap</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Old P-value</th>\n",
       "      <th>Old Adjusted P-value</th>\n",
       "      <th>Z-score</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Exosome  sce04147</td>\n",
       "      <td>7/198</td>\n",
       "      <td>8.788324e-07</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.001513</td>\n",
       "      <td>0.072619</td>\n",
       "      <td>-1.796607</td>\n",
       "      <td>25.053093</td>\n",
       "      <td>ACT1;YPT7;SSA3;TEF2;PPH21;YOP1;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Transporters  sce02000</td>\n",
       "      <td>6/223</td>\n",
       "      <td>2.662950e-05</td>\n",
       "      <td>0.000639</td>\n",
       "      <td>0.012430</td>\n",
       "      <td>0.198877</td>\n",
       "      <td>-1.495411</td>\n",
       "      <td>15.751902</td>\n",
       "      <td>TIM21;ENB1;ITR2;ERV14;VCX1;FET4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Sphingolipid metabolism  sce00600</td>\n",
       "      <td>2/15</td>\n",
       "      <td>7.076742e-04</td>\n",
       "      <td>0.008492</td>\n",
       "      <td>0.009147</td>\n",
       "      <td>0.198877</td>\n",
       "      <td>-1.915461</td>\n",
       "      <td>13.893845</td>\n",
       "      <td>LAC1;LAG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Membrane trafficking  sce04131</td>\n",
       "      <td>6/386</td>\n",
       "      <td>5.307089e-04</td>\n",
       "      <td>0.008491</td>\n",
       "      <td>0.117026</td>\n",
       "      <td>0.513072</td>\n",
       "      <td>-1.534199</td>\n",
       "      <td>11.569851</td>\n",
       "      <td>YPT7;SSA3;YIP1;TEF2;RHO1;ERV14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Alanine, aspartate and glutamate metabolism  s...</td>\n",
       "      <td>2/33</td>\n",
       "      <td>3.451648e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.036325</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.848719</td>\n",
       "      <td>10.480207</td>\n",
       "      <td>CPA1;GFA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Endocytosis  sce04144</td>\n",
       "      <td>3/75</td>\n",
       "      <td>1.036996e-03</td>\n",
       "      <td>0.009955</td>\n",
       "      <td>0.029072</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.371145</td>\n",
       "      <td>9.421723</td>\n",
       "      <td>YPT7;SSA3;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Various types of N-glycan biosynthesis  sce00513</td>\n",
       "      <td>2/31</td>\n",
       "      <td>3.050112e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.032582</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.542810</td>\n",
       "      <td>8.936845</td>\n",
       "      <td>ALG11;MNN10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Phagosome  sce04145</td>\n",
       "      <td>2/35</td>\n",
       "      <td>3.876500e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.040227</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.587780</td>\n",
       "      <td>8.816661</td>\n",
       "      <td>ACT1;YPT7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>GTP-binding proteins  sce04031</td>\n",
       "      <td>2/36</td>\n",
       "      <td>4.097591e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.042235</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.506383</td>\n",
       "      <td>8.281124</td>\n",
       "      <td>YPT7;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ether lipid metabolism  sce00565</td>\n",
       "      <td>1/8</td>\n",
       "      <td>2.100805e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.075514</td>\n",
       "      <td>0.402742</td>\n",
       "      <td>-1.824594</td>\n",
       "      <td>7.048131</td>\n",
       "      <td>CPT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Translation factors  sce03012</td>\n",
       "      <td>2/70</td>\n",
       "      <td>1.483219e-02</td>\n",
       "      <td>0.071195</td>\n",
       "      <td>0.128268</td>\n",
       "      <td>0.513072</td>\n",
       "      <td>-1.659375</td>\n",
       "      <td>6.987554</td>\n",
       "      <td>HYP2;TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Protein phosphatases and associated proteins  ...</td>\n",
       "      <td>2/89</td>\n",
       "      <td>2.329615e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.186124</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.445695</td>\n",
       "      <td>5.435042</td>\n",
       "      <td>SSA3;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Vitamin B6 metabolism  sce00750</td>\n",
       "      <td>1/14</td>\n",
       "      <td>3.647933e-02</td>\n",
       "      <td>0.112013</td>\n",
       "      <td>0.122756</td>\n",
       "      <td>0.513072</td>\n",
       "      <td>-1.555617</td>\n",
       "      <td>5.150663</td>\n",
       "      <td>BUD17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Steroid biosynthesis  sce00100</td>\n",
       "      <td>1/18</td>\n",
       "      <td>4.665998e-02</td>\n",
       "      <td>0.119311</td>\n",
       "      <td>0.152942</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.492773</td>\n",
       "      <td>4.575151</td>\n",
       "      <td>ERG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Autophagy - yeast  sce04138</td>\n",
       "      <td>2/86</td>\n",
       "      <td>2.185304e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.176717</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.167885</td>\n",
       "      <td>4.465310</td>\n",
       "      <td>YPT7;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>MAPK signaling pathway - yeast  sce04011</td>\n",
       "      <td>2/115</td>\n",
       "      <td>3.733755e-02</td>\n",
       "      <td>0.112013</td>\n",
       "      <td>0.269857</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>-1.191323</td>\n",
       "      <td>3.916778</td>\n",
       "      <td>SPA2;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Arginine and proline metabolism  sce00330</td>\n",
       "      <td>1/22</td>\n",
       "      <td>5.673507e-02</td>\n",
       "      <td>0.136164</td>\n",
       "      <td>0.182120</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.281387</td>\n",
       "      <td>3.676764</td>\n",
       "      <td>PUT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Mitochondrial biogenesis  sce03029</td>\n",
       "      <td>3/254</td>\n",
       "      <td>2.976862e-02</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.387649</td>\n",
       "      <td>0.664542</td>\n",
       "      <td>-1.000149</td>\n",
       "      <td>3.514824</td>\n",
       "      <td>ACT1;SSA3;TIM21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Cell cycle - yeast  sce04111</td>\n",
       "      <td>2/127</td>\n",
       "      <td>4.467252e-02</td>\n",
       "      <td>0.119311</td>\n",
       "      <td>0.308917</td>\n",
       "      <td>0.617834</td>\n",
       "      <td>-1.121502</td>\n",
       "      <td>3.486073</td>\n",
       "      <td>GRR1;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Citrate cycle (TCA cycle)  sce00020</td>\n",
       "      <td>1/33</td>\n",
       "      <td>8.390600e-02</td>\n",
       "      <td>0.172675</td>\n",
       "      <td>0.257430</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>-1.404926</td>\n",
       "      <td>3.481487</td>\n",
       "      <td>SHH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Meiosis - yeast  sce04113</td>\n",
       "      <td>2/131</td>\n",
       "      <td>4.722728e-02</td>\n",
       "      <td>0.119311</td>\n",
       "      <td>0.321884</td>\n",
       "      <td>0.618017</td>\n",
       "      <td>-1.077187</td>\n",
       "      <td>3.288417</td>\n",
       "      <td>DMC1;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Autophagy - other  sce04136</td>\n",
       "      <td>1/24</td>\n",
       "      <td>6.173337e-02</td>\n",
       "      <td>0.141105</td>\n",
       "      <td>0.196342</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.174693</td>\n",
       "      <td>3.271437</td>\n",
       "      <td>PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Amino sugar and nucleotide sugar metabolism  s...</td>\n",
       "      <td>1/34</td>\n",
       "      <td>8.633766e-02</td>\n",
       "      <td>0.172675</td>\n",
       "      <td>0.263933</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>-1.257325</td>\n",
       "      <td>3.079805</td>\n",
       "      <td>GFA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>N-Glycan biosynthesis  sce00510</td>\n",
       "      <td>1/31</td>\n",
       "      <td>7.902361e-02</td>\n",
       "      <td>0.172415</td>\n",
       "      <td>0.244256</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>-1.032902</td>\n",
       "      <td>2.621514</td>\n",
       "      <td>ALG11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Glycerophospholipid metabolism  sce00564</td>\n",
       "      <td>1/40</td>\n",
       "      <td>1.007952e-01</td>\n",
       "      <td>0.175966</td>\n",
       "      <td>0.301808</td>\n",
       "      <td>0.617834</td>\n",
       "      <td>-1.070079</td>\n",
       "      <td>2.455471</td>\n",
       "      <td>CPT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Longevity regulating pathway - multiple specie...</td>\n",
       "      <td>1/37</td>\n",
       "      <td>9.359471e-02</td>\n",
       "      <td>0.174255</td>\n",
       "      <td>0.283113</td>\n",
       "      <td>0.617700</td>\n",
       "      <td>-1.007971</td>\n",
       "      <td>2.387663</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Messenger RNA biogenesis  sce03019</td>\n",
       "      <td>2/205</td>\n",
       "      <td>1.026468e-01</td>\n",
       "      <td>0.175966</td>\n",
       "      <td>0.543849</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-1.048616</td>\n",
       "      <td>2.387135</td>\n",
       "      <td>ACT1;TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Transcription machinery  sce03021</td>\n",
       "      <td>2/195</td>\n",
       "      <td>9.438788e-02</td>\n",
       "      <td>0.174255</td>\n",
       "      <td>0.516583</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.822231</td>\n",
       "      <td>1.940748</td>\n",
       "      <td>ACT1;RGR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Proteasome  sce03051</td>\n",
       "      <td>1/58</td>\n",
       "      <td>1.428343e-01</td>\n",
       "      <td>0.221163</td>\n",
       "      <td>0.404449</td>\n",
       "      <td>0.669433</td>\n",
       "      <td>-0.879098</td>\n",
       "      <td>1.710787</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>mRNA surveillance pathway  sce03015</td>\n",
       "      <td>1/47</td>\n",
       "      <td>1.173788e-01</td>\n",
       "      <td>0.191557</td>\n",
       "      <td>0.343612</td>\n",
       "      <td>0.621125</td>\n",
       "      <td>-0.796126</td>\n",
       "      <td>1.705581</td>\n",
       "      <td>PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ubiquitin mediated proteolysis  sce04120</td>\n",
       "      <td>1/48</td>\n",
       "      <td>1.197233e-01</td>\n",
       "      <td>0.191557</td>\n",
       "      <td>0.349383</td>\n",
       "      <td>0.621125</td>\n",
       "      <td>-0.773774</td>\n",
       "      <td>1.642392</td>\n",
       "      <td>GRR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Cytoskeleton proteins  sce04812</td>\n",
       "      <td>1/65</td>\n",
       "      <td>1.586568e-01</td>\n",
       "      <td>0.230773</td>\n",
       "      <td>0.440280</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.694820</td>\n",
       "      <td>1.279172</td>\n",
       "      <td>ACT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Protein kinases  sce01001</td>\n",
       "      <td>1/86</td>\n",
       "      <td>2.044245e-01</td>\n",
       "      <td>0.254281</td>\n",
       "      <td>0.535686</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.680801</td>\n",
       "      <td>1.080810</td>\n",
       "      <td>YPK3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Glycosyltransferases  sce01003</td>\n",
       "      <td>1/63</td>\n",
       "      <td>1.541655e-01</td>\n",
       "      <td>0.230773</td>\n",
       "      <td>0.430262</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.577463</td>\n",
       "      <td>1.079698</td>\n",
       "      <td>ALG11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Pyrimidine metabolism  sce00240</td>\n",
       "      <td>1/72</td>\n",
       "      <td>1.741925e-01</td>\n",
       "      <td>0.238106</td>\n",
       "      <td>0.474019</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.605458</td>\n",
       "      <td>1.058096</td>\n",
       "      <td>CPA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Oxidative phosphorylation  sce00190</td>\n",
       "      <td>1/73</td>\n",
       "      <td>1.763888e-01</td>\n",
       "      <td>0.238106</td>\n",
       "      <td>0.478675</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.581687</td>\n",
       "      <td>1.009264</td>\n",
       "      <td>SHH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Chaperones and folding catalysts  sce03110</td>\n",
       "      <td>1/74</td>\n",
       "      <td>1.785794e-01</td>\n",
       "      <td>0.238106</td>\n",
       "      <td>0.483291</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.570718</td>\n",
       "      <td>0.983189</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Spliceosome  sce03040</td>\n",
       "      <td>1/80</td>\n",
       "      <td>1.916033e-01</td>\n",
       "      <td>0.248566</td>\n",
       "      <td>0.510167</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.574151</td>\n",
       "      <td>0.948687</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Protein processing in endoplasmic reticulum  s...</td>\n",
       "      <td>1/89</td>\n",
       "      <td>2.107600e-01</td>\n",
       "      <td>0.254281</td>\n",
       "      <td>0.547958</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.562006</td>\n",
       "      <td>0.875064</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Chromosome and associated proteins  sce03036</td>\n",
       "      <td>2/324</td>\n",
       "      <td>2.119009e-01</td>\n",
       "      <td>0.254281</td>\n",
       "      <td>0.789012</td>\n",
       "      <td>0.805799</td>\n",
       "      <td>-0.543684</td>\n",
       "      <td>0.843599</td>\n",
       "      <td>ACT1;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>RNA transport  sce03013</td>\n",
       "      <td>1/94</td>\n",
       "      <td>2.212095e-01</td>\n",
       "      <td>0.258977</td>\n",
       "      <td>0.567717</td>\n",
       "      <td>0.681260</td>\n",
       "      <td>-0.531269</td>\n",
       "      <td>0.801496</td>\n",
       "      <td>TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Peptidases  sce01002</td>\n",
       "      <td>1/100</td>\n",
       "      <td>2.335697e-01</td>\n",
       "      <td>0.266937</td>\n",
       "      <td>0.590324</td>\n",
       "      <td>0.686794</td>\n",
       "      <td>-0.502781</td>\n",
       "      <td>0.731182</td>\n",
       "      <td>GFA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Enzymes with EC numbers sce99980</td>\n",
       "      <td>1/104</td>\n",
       "      <td>2.417028e-01</td>\n",
       "      <td>0.269808</td>\n",
       "      <td>0.604754</td>\n",
       "      <td>0.686794</td>\n",
       "      <td>-0.366287</td>\n",
       "      <td>0.520145</td>\n",
       "      <td>SCS7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Spliceosome  sce03041</td>\n",
       "      <td>1/107</td>\n",
       "      <td>2.477469e-01</td>\n",
       "      <td>0.270269</td>\n",
       "      <td>0.615253</td>\n",
       "      <td>0.686794</td>\n",
       "      <td>-0.311198</td>\n",
       "      <td>0.434229</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ubiquitin system  sce04121</td>\n",
       "      <td>1/121</td>\n",
       "      <td>2.753337e-01</td>\n",
       "      <td>0.293689</td>\n",
       "      <td>0.660778</td>\n",
       "      <td>0.720849</td>\n",
       "      <td>-0.313987</td>\n",
       "      <td>0.404972</td>\n",
       "      <td>GRR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>DNA repair and recombination proteins  sce03400</td>\n",
       "      <td>1/154</td>\n",
       "      <td>3.364923e-01</td>\n",
       "      <td>0.343652</td>\n",
       "      <td>0.748401</td>\n",
       "      <td>0.780940</td>\n",
       "      <td>-0.155383</td>\n",
       "      <td>0.169240</td>\n",
       "      <td>DMC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Transfer RNA biogenesis  sce03016</td>\n",
       "      <td>1/149</td>\n",
       "      <td>3.275626e-01</td>\n",
       "      <td>0.341804</td>\n",
       "      <td>0.736700</td>\n",
       "      <td>0.780940</td>\n",
       "      <td>-0.044488</td>\n",
       "      <td>0.049652</td>\n",
       "      <td>TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ribosome biogenesis  sce03009</td>\n",
       "      <td>1/308</td>\n",
       "      <td>5.611640e-01</td>\n",
       "      <td>0.561164</td>\n",
       "      <td>0.939946</td>\n",
       "      <td>0.939946</td>\n",
       "      <td>0.413236</td>\n",
       "      <td>-0.238744</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gene_set                                               Term Overlap  \\\n",
       "0   KEGG_2018                                  Exosome  sce04147   7/198   \n",
       "1   KEGG_2018                             Transporters  sce02000   6/223   \n",
       "2   KEGG_2018                  Sphingolipid metabolism  sce00600    2/15   \n",
       "3   KEGG_2018                     Membrane trafficking  sce04131   6/386   \n",
       "4   KEGG_2018  Alanine, aspartate and glutamate metabolism  s...    2/33   \n",
       "5   KEGG_2018                              Endocytosis  sce04144    3/75   \n",
       "6   KEGG_2018   Various types of N-glycan biosynthesis  sce00513    2/31   \n",
       "7   KEGG_2018                                Phagosome  sce04145    2/35   \n",
       "8   KEGG_2018                     GTP-binding proteins  sce04031    2/36   \n",
       "9   KEGG_2018                   Ether lipid metabolism  sce00565     1/8   \n",
       "10  KEGG_2018                      Translation factors  sce03012    2/70   \n",
       "11  KEGG_2018  Protein phosphatases and associated proteins  ...    2/89   \n",
       "12  KEGG_2018                    Vitamin B6 metabolism  sce00750    1/14   \n",
       "13  KEGG_2018                     Steroid biosynthesis  sce00100    1/18   \n",
       "14  KEGG_2018                        Autophagy - yeast  sce04138    2/86   \n",
       "15  KEGG_2018           MAPK signaling pathway - yeast  sce04011   2/115   \n",
       "16  KEGG_2018          Arginine and proline metabolism  sce00330    1/22   \n",
       "17  KEGG_2018                 Mitochondrial biogenesis  sce03029   3/254   \n",
       "18  KEGG_2018                       Cell cycle - yeast  sce04111   2/127   \n",
       "19  KEGG_2018                Citrate cycle (TCA cycle)  sce00020    1/33   \n",
       "20  KEGG_2018                          Meiosis - yeast  sce04113   2/131   \n",
       "21  KEGG_2018                        Autophagy - other  sce04136    1/24   \n",
       "22  KEGG_2018  Amino sugar and nucleotide sugar metabolism  s...    1/34   \n",
       "23  KEGG_2018                    N-Glycan biosynthesis  sce00510    1/31   \n",
       "24  KEGG_2018           Glycerophospholipid metabolism  sce00564    1/40   \n",
       "25  KEGG_2018  Longevity regulating pathway - multiple specie...    1/37   \n",
       "26  KEGG_2018                 Messenger RNA biogenesis  sce03019   2/205   \n",
       "27  KEGG_2018                  Transcription machinery  sce03021   2/195   \n",
       "28  KEGG_2018                               Proteasome  sce03051    1/58   \n",
       "29  KEGG_2018                mRNA surveillance pathway  sce03015    1/47   \n",
       "30  KEGG_2018           Ubiquitin mediated proteolysis  sce04120    1/48   \n",
       "31  KEGG_2018                    Cytoskeleton proteins  sce04812    1/65   \n",
       "32  KEGG_2018                          Protein kinases  sce01001    1/86   \n",
       "33  KEGG_2018                     Glycosyltransferases  sce01003    1/63   \n",
       "34  KEGG_2018                    Pyrimidine metabolism  sce00240    1/72   \n",
       "35  KEGG_2018                Oxidative phosphorylation  sce00190    1/73   \n",
       "36  KEGG_2018         Chaperones and folding catalysts  sce03110    1/74   \n",
       "37  KEGG_2018                              Spliceosome  sce03040    1/80   \n",
       "38  KEGG_2018  Protein processing in endoplasmic reticulum  s...    1/89   \n",
       "39  KEGG_2018       Chromosome and associated proteins  sce03036   2/324   \n",
       "40  KEGG_2018                            RNA transport  sce03013    1/94   \n",
       "41  KEGG_2018                               Peptidases  sce01002   1/100   \n",
       "42  KEGG_2018                   Enzymes with EC numbers sce99980   1/104   \n",
       "43  KEGG_2018                              Spliceosome  sce03041   1/107   \n",
       "44  KEGG_2018                         Ubiquitin system  sce04121   1/121   \n",
       "45  KEGG_2018    DNA repair and recombination proteins  sce03400   1/154   \n",
       "46  KEGG_2018                  Transfer RNA biogenesis  sce03016   1/149   \n",
       "47  KEGG_2018                      Ribosome biogenesis  sce03009   1/308   \n",
       "\n",
       "         P-value  Adjusted P-value  Old P-value  Old Adjusted P-value  \\\n",
       "0   8.788324e-07          0.000042     0.001513              0.072619   \n",
       "1   2.662950e-05          0.000639     0.012430              0.198877   \n",
       "2   7.076742e-04          0.008492     0.009147              0.198877   \n",
       "3   5.307089e-04          0.008491     0.117026              0.513072   \n",
       "4   3.451648e-03          0.021854     0.036325              0.253413   \n",
       "5   1.036996e-03          0.009955     0.029072              0.253413   \n",
       "6   3.050112e-03          0.021854     0.032582              0.253413   \n",
       "7   3.876500e-03          0.021854     0.040227              0.253413   \n",
       "8   4.097591e-03          0.021854     0.042235              0.253413   \n",
       "9   2.100805e-02          0.086017     0.075514              0.402742   \n",
       "10  1.483219e-02          0.071195     0.128268              0.513072   \n",
       "11  2.329615e-02          0.086017     0.186124              0.554377   \n",
       "12  3.647933e-02          0.112013     0.122756              0.513072   \n",
       "13  4.665998e-02          0.119311     0.152942              0.554377   \n",
       "14  2.185304e-02          0.086017     0.176717              0.554377   \n",
       "15  3.733755e-02          0.112013     0.269857              0.616816   \n",
       "16  5.673507e-02          0.136164     0.182120              0.554377   \n",
       "17  2.976862e-02          0.102064     0.387649              0.664542   \n",
       "18  4.467252e-02          0.119311     0.308917              0.617834   \n",
       "19  8.390600e-02          0.172675     0.257430              0.616816   \n",
       "20  4.722728e-02          0.119311     0.321884              0.618017   \n",
       "21  6.173337e-02          0.141105     0.196342              0.554377   \n",
       "22  8.633766e-02          0.172675     0.263933              0.616816   \n",
       "23  7.902361e-02          0.172415     0.244256              0.616816   \n",
       "24  1.007952e-01          0.175966     0.301808              0.617834   \n",
       "25  9.359471e-02          0.174255     0.283113              0.617700   \n",
       "26  1.026468e-01          0.175966     0.543849              0.674410   \n",
       "27  9.438788e-02          0.174255     0.516583              0.674410   \n",
       "28  1.428343e-01          0.221163     0.404449              0.669433   \n",
       "29  1.173788e-01          0.191557     0.343612              0.621125   \n",
       "30  1.197233e-01          0.191557     0.349383              0.621125   \n",
       "31  1.586568e-01          0.230773     0.440280              0.674410   \n",
       "32  2.044245e-01          0.254281     0.535686              0.674410   \n",
       "33  1.541655e-01          0.230773     0.430262              0.674410   \n",
       "34  1.741925e-01          0.238106     0.474019              0.674410   \n",
       "35  1.763888e-01          0.238106     0.478675              0.674410   \n",
       "36  1.785794e-01          0.238106     0.483291              0.674410   \n",
       "37  1.916033e-01          0.248566     0.510167              0.674410   \n",
       "38  2.107600e-01          0.254281     0.547958              0.674410   \n",
       "39  2.119009e-01          0.254281     0.789012              0.805799   \n",
       "40  2.212095e-01          0.258977     0.567717              0.681260   \n",
       "41  2.335697e-01          0.266937     0.590324              0.686794   \n",
       "42  2.417028e-01          0.269808     0.604754              0.686794   \n",
       "43  2.477469e-01          0.270269     0.615253              0.686794   \n",
       "44  2.753337e-01          0.293689     0.660778              0.720849   \n",
       "45  3.364923e-01          0.343652     0.748401              0.780940   \n",
       "46  3.275626e-01          0.341804     0.736700              0.780940   \n",
       "47  5.611640e-01          0.561164     0.939946              0.939946   \n",
       "\n",
       "     Z-score  Combined Score                                Genes  \n",
       "0  -1.796607       25.053093  ACT1;YPT7;SSA3;TEF2;PPH21;YOP1;RHO1  \n",
       "1  -1.495411       15.751902      TIM21;ENB1;ITR2;ERV14;VCX1;FET4  \n",
       "2  -1.915461       13.893845                            LAC1;LAG1  \n",
       "3  -1.534199       11.569851       YPT7;SSA3;YIP1;TEF2;RHO1;ERV14  \n",
       "4  -1.848719       10.480207                            CPA1;GFA1  \n",
       "5  -1.371145        9.421723                       YPT7;SSA3;RHO1  \n",
       "6  -1.542810        8.936845                          ALG11;MNN10  \n",
       "7  -1.587780        8.816661                            ACT1;YPT7  \n",
       "8  -1.506383        8.281124                            YPT7;RHO1  \n",
       "9  -1.824594        7.048131                                 CPT1  \n",
       "10 -1.659375        6.987554                            HYP2;TEF2  \n",
       "11 -1.445695        5.435042                           SSA3;PPH21  \n",
       "12 -1.555617        5.150663                                BUD17  \n",
       "13 -1.492773        4.575151                                 ERG1  \n",
       "14 -1.167885        4.465310                           YPT7;PPH21  \n",
       "15 -1.191323        3.916778                            SPA2;RHO1  \n",
       "16 -1.281387        3.676764                                 PUT1  \n",
       "17 -1.000149        3.514824                      ACT1;SSA3;TIM21  \n",
       "18 -1.121502        3.486073                           GRR1;PPH21  \n",
       "19 -1.404926        3.481487                                 SHH3  \n",
       "20 -1.077187        3.288417                           DMC1;PPH21  \n",
       "21 -1.174693        3.271437                                PPH21  \n",
       "22 -1.257325        3.079805                                 GFA1  \n",
       "23 -1.032902        2.621514                                ALG11  \n",
       "24 -1.070079        2.455471                                 CPT1  \n",
       "25 -1.007971        2.387663                                 SSA3  \n",
       "26 -1.048616        2.387135                            ACT1;TEF2  \n",
       "27 -0.822231        1.940748                            ACT1;RGR1  \n",
       "28 -0.879098        1.710787                                 SSA3  \n",
       "29 -0.796126        1.705581                                PPH21  \n",
       "30 -0.773774        1.642392                                 GRR1  \n",
       "31 -0.694820        1.279172                                 ACT1  \n",
       "32 -0.680801        1.080810                                 YPK3  \n",
       "33 -0.577463        1.079698                                ALG11  \n",
       "34 -0.605458        1.058096                                 CPA1  \n",
       "35 -0.581687        1.009264                                 SHH3  \n",
       "36 -0.570718        0.983189                                 SSA3  \n",
       "37 -0.574151        0.948687                                 SSA3  \n",
       "38 -0.562006        0.875064                                 SSA3  \n",
       "39 -0.543684        0.843599                           ACT1;PPH21  \n",
       "40 -0.531269        0.801496                                 TEF2  \n",
       "41 -0.502781        0.731182                                 GFA1  \n",
       "42 -0.366287        0.520145                                 SCS7  \n",
       "43 -0.311198        0.434229                                 SSA3  \n",
       "44 -0.313987        0.404972                                 GRR1  \n",
       "45 -0.155383        0.169240                                 DMC1  \n",
       "46 -0.044488        0.049652                                 TEF2  \n",
       "47  0.413236       -0.238744                                 SSA3  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enr.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>Overlap</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Old P-value</th>\n",
       "      <th>Old Adjusted P-value</th>\n",
       "      <th>Z-score</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Exosome  sce04147</td>\n",
       "      <td>7/198</td>\n",
       "      <td>8.788324e-07</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.001513</td>\n",
       "      <td>0.072619</td>\n",
       "      <td>-1.796607</td>\n",
       "      <td>25.053093</td>\n",
       "      <td>ACT1;YPT7;SSA3;TEF2;PPH21;YOP1;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Transporters  sce02000</td>\n",
       "      <td>6/223</td>\n",
       "      <td>2.662950e-05</td>\n",
       "      <td>0.000639</td>\n",
       "      <td>0.012430</td>\n",
       "      <td>0.198877</td>\n",
       "      <td>-1.495411</td>\n",
       "      <td>15.751902</td>\n",
       "      <td>TIM21;ENB1;ITR2;ERV14;VCX1;FET4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Sphingolipid metabolism  sce00600</td>\n",
       "      <td>2/15</td>\n",
       "      <td>7.076742e-04</td>\n",
       "      <td>0.008492</td>\n",
       "      <td>0.009147</td>\n",
       "      <td>0.198877</td>\n",
       "      <td>-1.915461</td>\n",
       "      <td>13.893845</td>\n",
       "      <td>LAC1;LAG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Membrane trafficking  sce04131</td>\n",
       "      <td>6/386</td>\n",
       "      <td>5.307089e-04</td>\n",
       "      <td>0.008491</td>\n",
       "      <td>0.117026</td>\n",
       "      <td>0.513072</td>\n",
       "      <td>-1.534199</td>\n",
       "      <td>11.569851</td>\n",
       "      <td>YPT7;SSA3;YIP1;TEF2;RHO1;ERV14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Alanine, aspartate and glutamate metabolism  s...</td>\n",
       "      <td>2/33</td>\n",
       "      <td>3.451648e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.036325</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.848719</td>\n",
       "      <td>10.480207</td>\n",
       "      <td>CPA1;GFA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Endocytosis  sce04144</td>\n",
       "      <td>3/75</td>\n",
       "      <td>1.036996e-03</td>\n",
       "      <td>0.009955</td>\n",
       "      <td>0.029072</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.371145</td>\n",
       "      <td>9.421723</td>\n",
       "      <td>YPT7;SSA3;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Various types of N-glycan biosynthesis  sce00513</td>\n",
       "      <td>2/31</td>\n",
       "      <td>3.050112e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.032582</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.542810</td>\n",
       "      <td>8.936845</td>\n",
       "      <td>ALG11;MNN10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Phagosome  sce04145</td>\n",
       "      <td>2/35</td>\n",
       "      <td>3.876500e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.040227</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.587780</td>\n",
       "      <td>8.816661</td>\n",
       "      <td>ACT1;YPT7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>GTP-binding proteins  sce04031</td>\n",
       "      <td>2/36</td>\n",
       "      <td>4.097591e-03</td>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.042235</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-1.506383</td>\n",
       "      <td>8.281124</td>\n",
       "      <td>YPT7;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ether lipid metabolism  sce00565</td>\n",
       "      <td>1/8</td>\n",
       "      <td>2.100805e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.075514</td>\n",
       "      <td>0.402742</td>\n",
       "      <td>-1.824594</td>\n",
       "      <td>7.048131</td>\n",
       "      <td>CPT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Translation factors  sce03012</td>\n",
       "      <td>2/70</td>\n",
       "      <td>1.483219e-02</td>\n",
       "      <td>0.071195</td>\n",
       "      <td>0.128268</td>\n",
       "      <td>0.513072</td>\n",
       "      <td>-1.659375</td>\n",
       "      <td>6.987554</td>\n",
       "      <td>HYP2;TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Protein phosphatases and associated proteins  ...</td>\n",
       "      <td>2/89</td>\n",
       "      <td>2.329615e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.186124</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.445695</td>\n",
       "      <td>5.435042</td>\n",
       "      <td>SSA3;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Vitamin B6 metabolism  sce00750</td>\n",
       "      <td>1/14</td>\n",
       "      <td>3.647933e-02</td>\n",
       "      <td>0.112013</td>\n",
       "      <td>0.122756</td>\n",
       "      <td>0.513072</td>\n",
       "      <td>-1.555617</td>\n",
       "      <td>5.150663</td>\n",
       "      <td>BUD17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Steroid biosynthesis  sce00100</td>\n",
       "      <td>1/18</td>\n",
       "      <td>4.665998e-02</td>\n",
       "      <td>0.119311</td>\n",
       "      <td>0.152942</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.492773</td>\n",
       "      <td>4.575151</td>\n",
       "      <td>ERG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Autophagy - yeast  sce04138</td>\n",
       "      <td>2/86</td>\n",
       "      <td>2.185304e-02</td>\n",
       "      <td>0.086017</td>\n",
       "      <td>0.176717</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.167885</td>\n",
       "      <td>4.465310</td>\n",
       "      <td>YPT7;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>MAPK signaling pathway - yeast  sce04011</td>\n",
       "      <td>2/115</td>\n",
       "      <td>3.733755e-02</td>\n",
       "      <td>0.112013</td>\n",
       "      <td>0.269857</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>-1.191323</td>\n",
       "      <td>3.916778</td>\n",
       "      <td>SPA2;RHO1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Arginine and proline metabolism  sce00330</td>\n",
       "      <td>1/22</td>\n",
       "      <td>5.673507e-02</td>\n",
       "      <td>0.136164</td>\n",
       "      <td>0.182120</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.281387</td>\n",
       "      <td>3.676764</td>\n",
       "      <td>PUT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Mitochondrial biogenesis  sce03029</td>\n",
       "      <td>3/254</td>\n",
       "      <td>2.976862e-02</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.387649</td>\n",
       "      <td>0.664542</td>\n",
       "      <td>-1.000149</td>\n",
       "      <td>3.514824</td>\n",
       "      <td>ACT1;SSA3;TIM21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Cell cycle - yeast  sce04111</td>\n",
       "      <td>2/127</td>\n",
       "      <td>4.467252e-02</td>\n",
       "      <td>0.119311</td>\n",
       "      <td>0.308917</td>\n",
       "      <td>0.617834</td>\n",
       "      <td>-1.121502</td>\n",
       "      <td>3.486073</td>\n",
       "      <td>GRR1;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Citrate cycle (TCA cycle)  sce00020</td>\n",
       "      <td>1/33</td>\n",
       "      <td>8.390600e-02</td>\n",
       "      <td>0.172675</td>\n",
       "      <td>0.257430</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>-1.404926</td>\n",
       "      <td>3.481487</td>\n",
       "      <td>SHH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Meiosis - yeast  sce04113</td>\n",
       "      <td>2/131</td>\n",
       "      <td>4.722728e-02</td>\n",
       "      <td>0.119311</td>\n",
       "      <td>0.321884</td>\n",
       "      <td>0.618017</td>\n",
       "      <td>-1.077187</td>\n",
       "      <td>3.288417</td>\n",
       "      <td>DMC1;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Autophagy - other  sce04136</td>\n",
       "      <td>1/24</td>\n",
       "      <td>6.173337e-02</td>\n",
       "      <td>0.141105</td>\n",
       "      <td>0.196342</td>\n",
       "      <td>0.554377</td>\n",
       "      <td>-1.174693</td>\n",
       "      <td>3.271437</td>\n",
       "      <td>PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Amino sugar and nucleotide sugar metabolism  s...</td>\n",
       "      <td>1/34</td>\n",
       "      <td>8.633766e-02</td>\n",
       "      <td>0.172675</td>\n",
       "      <td>0.263933</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>-1.257325</td>\n",
       "      <td>3.079805</td>\n",
       "      <td>GFA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>N-Glycan biosynthesis  sce00510</td>\n",
       "      <td>1/31</td>\n",
       "      <td>7.902361e-02</td>\n",
       "      <td>0.172415</td>\n",
       "      <td>0.244256</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>-1.032902</td>\n",
       "      <td>2.621514</td>\n",
       "      <td>ALG11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Glycerophospholipid metabolism  sce00564</td>\n",
       "      <td>1/40</td>\n",
       "      <td>1.007952e-01</td>\n",
       "      <td>0.175966</td>\n",
       "      <td>0.301808</td>\n",
       "      <td>0.617834</td>\n",
       "      <td>-1.070079</td>\n",
       "      <td>2.455471</td>\n",
       "      <td>CPT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Longevity regulating pathway - multiple specie...</td>\n",
       "      <td>1/37</td>\n",
       "      <td>9.359471e-02</td>\n",
       "      <td>0.174255</td>\n",
       "      <td>0.283113</td>\n",
       "      <td>0.617700</td>\n",
       "      <td>-1.007971</td>\n",
       "      <td>2.387663</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Messenger RNA biogenesis  sce03019</td>\n",
       "      <td>2/205</td>\n",
       "      <td>1.026468e-01</td>\n",
       "      <td>0.175966</td>\n",
       "      <td>0.543849</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-1.048616</td>\n",
       "      <td>2.387135</td>\n",
       "      <td>ACT1;TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Transcription machinery  sce03021</td>\n",
       "      <td>2/195</td>\n",
       "      <td>9.438788e-02</td>\n",
       "      <td>0.174255</td>\n",
       "      <td>0.516583</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.822231</td>\n",
       "      <td>1.940748</td>\n",
       "      <td>ACT1;RGR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Proteasome  sce03051</td>\n",
       "      <td>1/58</td>\n",
       "      <td>1.428343e-01</td>\n",
       "      <td>0.221163</td>\n",
       "      <td>0.404449</td>\n",
       "      <td>0.669433</td>\n",
       "      <td>-0.879098</td>\n",
       "      <td>1.710787</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>mRNA surveillance pathway  sce03015</td>\n",
       "      <td>1/47</td>\n",
       "      <td>1.173788e-01</td>\n",
       "      <td>0.191557</td>\n",
       "      <td>0.343612</td>\n",
       "      <td>0.621125</td>\n",
       "      <td>-0.796126</td>\n",
       "      <td>1.705581</td>\n",
       "      <td>PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ubiquitin mediated proteolysis  sce04120</td>\n",
       "      <td>1/48</td>\n",
       "      <td>1.197233e-01</td>\n",
       "      <td>0.191557</td>\n",
       "      <td>0.349383</td>\n",
       "      <td>0.621125</td>\n",
       "      <td>-0.773774</td>\n",
       "      <td>1.642392</td>\n",
       "      <td>GRR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Cytoskeleton proteins  sce04812</td>\n",
       "      <td>1/65</td>\n",
       "      <td>1.586568e-01</td>\n",
       "      <td>0.230773</td>\n",
       "      <td>0.440280</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.694820</td>\n",
       "      <td>1.279172</td>\n",
       "      <td>ACT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Protein kinases  sce01001</td>\n",
       "      <td>1/86</td>\n",
       "      <td>2.044245e-01</td>\n",
       "      <td>0.254281</td>\n",
       "      <td>0.535686</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.680801</td>\n",
       "      <td>1.080810</td>\n",
       "      <td>YPK3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Glycosyltransferases  sce01003</td>\n",
       "      <td>1/63</td>\n",
       "      <td>1.541655e-01</td>\n",
       "      <td>0.230773</td>\n",
       "      <td>0.430262</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.577463</td>\n",
       "      <td>1.079698</td>\n",
       "      <td>ALG11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Pyrimidine metabolism  sce00240</td>\n",
       "      <td>1/72</td>\n",
       "      <td>1.741925e-01</td>\n",
       "      <td>0.238106</td>\n",
       "      <td>0.474019</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.605458</td>\n",
       "      <td>1.058096</td>\n",
       "      <td>CPA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Oxidative phosphorylation  sce00190</td>\n",
       "      <td>1/73</td>\n",
       "      <td>1.763888e-01</td>\n",
       "      <td>0.238106</td>\n",
       "      <td>0.478675</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.581687</td>\n",
       "      <td>1.009264</td>\n",
       "      <td>SHH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Chaperones and folding catalysts  sce03110</td>\n",
       "      <td>1/74</td>\n",
       "      <td>1.785794e-01</td>\n",
       "      <td>0.238106</td>\n",
       "      <td>0.483291</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.570718</td>\n",
       "      <td>0.983189</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Spliceosome  sce03040</td>\n",
       "      <td>1/80</td>\n",
       "      <td>1.916033e-01</td>\n",
       "      <td>0.248566</td>\n",
       "      <td>0.510167</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.574151</td>\n",
       "      <td>0.948687</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Protein processing in endoplasmic reticulum  s...</td>\n",
       "      <td>1/89</td>\n",
       "      <td>2.107600e-01</td>\n",
       "      <td>0.254281</td>\n",
       "      <td>0.547958</td>\n",
       "      <td>0.674410</td>\n",
       "      <td>-0.562006</td>\n",
       "      <td>0.875064</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Chromosome and associated proteins  sce03036</td>\n",
       "      <td>2/324</td>\n",
       "      <td>2.119009e-01</td>\n",
       "      <td>0.254281</td>\n",
       "      <td>0.789012</td>\n",
       "      <td>0.805799</td>\n",
       "      <td>-0.543684</td>\n",
       "      <td>0.843599</td>\n",
       "      <td>ACT1;PPH21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>RNA transport  sce03013</td>\n",
       "      <td>1/94</td>\n",
       "      <td>2.212095e-01</td>\n",
       "      <td>0.258977</td>\n",
       "      <td>0.567717</td>\n",
       "      <td>0.681260</td>\n",
       "      <td>-0.531269</td>\n",
       "      <td>0.801496</td>\n",
       "      <td>TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Peptidases  sce01002</td>\n",
       "      <td>1/100</td>\n",
       "      <td>2.335697e-01</td>\n",
       "      <td>0.266937</td>\n",
       "      <td>0.590324</td>\n",
       "      <td>0.686794</td>\n",
       "      <td>-0.502781</td>\n",
       "      <td>0.731182</td>\n",
       "      <td>GFA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Enzymes with EC numbers sce99980</td>\n",
       "      <td>1/104</td>\n",
       "      <td>2.417028e-01</td>\n",
       "      <td>0.269808</td>\n",
       "      <td>0.604754</td>\n",
       "      <td>0.686794</td>\n",
       "      <td>-0.366287</td>\n",
       "      <td>0.520145</td>\n",
       "      <td>SCS7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Spliceosome  sce03041</td>\n",
       "      <td>1/107</td>\n",
       "      <td>2.477469e-01</td>\n",
       "      <td>0.270269</td>\n",
       "      <td>0.615253</td>\n",
       "      <td>0.686794</td>\n",
       "      <td>-0.311198</td>\n",
       "      <td>0.434229</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ubiquitin system  sce04121</td>\n",
       "      <td>1/121</td>\n",
       "      <td>2.753337e-01</td>\n",
       "      <td>0.293689</td>\n",
       "      <td>0.660778</td>\n",
       "      <td>0.720849</td>\n",
       "      <td>-0.313987</td>\n",
       "      <td>0.404972</td>\n",
       "      <td>GRR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>DNA repair and recombination proteins  sce03400</td>\n",
       "      <td>1/154</td>\n",
       "      <td>3.364923e-01</td>\n",
       "      <td>0.343652</td>\n",
       "      <td>0.748401</td>\n",
       "      <td>0.780940</td>\n",
       "      <td>-0.155383</td>\n",
       "      <td>0.169240</td>\n",
       "      <td>DMC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Transfer RNA biogenesis  sce03016</td>\n",
       "      <td>1/149</td>\n",
       "      <td>3.275626e-01</td>\n",
       "      <td>0.341804</td>\n",
       "      <td>0.736700</td>\n",
       "      <td>0.780940</td>\n",
       "      <td>-0.044488</td>\n",
       "      <td>0.049652</td>\n",
       "      <td>TEF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>KEGG_2018</td>\n",
       "      <td>Ribosome biogenesis  sce03009</td>\n",
       "      <td>1/308</td>\n",
       "      <td>5.611640e-01</td>\n",
       "      <td>0.561164</td>\n",
       "      <td>0.939946</td>\n",
       "      <td>0.939946</td>\n",
       "      <td>0.413236</td>\n",
       "      <td>-0.238744</td>\n",
       "      <td>SSA3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gene_set                                               Term Overlap  \\\n",
       "0   KEGG_2018                                  Exosome  sce04147   7/198   \n",
       "1   KEGG_2018                             Transporters  sce02000   6/223   \n",
       "2   KEGG_2018                  Sphingolipid metabolism  sce00600    2/15   \n",
       "3   KEGG_2018                     Membrane trafficking  sce04131   6/386   \n",
       "4   KEGG_2018  Alanine, aspartate and glutamate metabolism  s...    2/33   \n",
       "5   KEGG_2018                              Endocytosis  sce04144    3/75   \n",
       "6   KEGG_2018   Various types of N-glycan biosynthesis  sce00513    2/31   \n",
       "7   KEGG_2018                                Phagosome  sce04145    2/35   \n",
       "8   KEGG_2018                     GTP-binding proteins  sce04031    2/36   \n",
       "9   KEGG_2018                   Ether lipid metabolism  sce00565     1/8   \n",
       "10  KEGG_2018                      Translation factors  sce03012    2/70   \n",
       "11  KEGG_2018  Protein phosphatases and associated proteins  ...    2/89   \n",
       "12  KEGG_2018                    Vitamin B6 metabolism  sce00750    1/14   \n",
       "13  KEGG_2018                     Steroid biosynthesis  sce00100    1/18   \n",
       "14  KEGG_2018                        Autophagy - yeast  sce04138    2/86   \n",
       "15  KEGG_2018           MAPK signaling pathway - yeast  sce04011   2/115   \n",
       "16  KEGG_2018          Arginine and proline metabolism  sce00330    1/22   \n",
       "17  KEGG_2018                 Mitochondrial biogenesis  sce03029   3/254   \n",
       "18  KEGG_2018                       Cell cycle - yeast  sce04111   2/127   \n",
       "19  KEGG_2018                Citrate cycle (TCA cycle)  sce00020    1/33   \n",
       "20  KEGG_2018                          Meiosis - yeast  sce04113   2/131   \n",
       "21  KEGG_2018                        Autophagy - other  sce04136    1/24   \n",
       "22  KEGG_2018  Amino sugar and nucleotide sugar metabolism  s...    1/34   \n",
       "23  KEGG_2018                    N-Glycan biosynthesis  sce00510    1/31   \n",
       "24  KEGG_2018           Glycerophospholipid metabolism  sce00564    1/40   \n",
       "25  KEGG_2018  Longevity regulating pathway - multiple specie...    1/37   \n",
       "26  KEGG_2018                 Messenger RNA biogenesis  sce03019   2/205   \n",
       "27  KEGG_2018                  Transcription machinery  sce03021   2/195   \n",
       "28  KEGG_2018                               Proteasome  sce03051    1/58   \n",
       "29  KEGG_2018                mRNA surveillance pathway  sce03015    1/47   \n",
       "30  KEGG_2018           Ubiquitin mediated proteolysis  sce04120    1/48   \n",
       "31  KEGG_2018                    Cytoskeleton proteins  sce04812    1/65   \n",
       "32  KEGG_2018                          Protein kinases  sce01001    1/86   \n",
       "33  KEGG_2018                     Glycosyltransferases  sce01003    1/63   \n",
       "34  KEGG_2018                    Pyrimidine metabolism  sce00240    1/72   \n",
       "35  KEGG_2018                Oxidative phosphorylation  sce00190    1/73   \n",
       "36  KEGG_2018         Chaperones and folding catalysts  sce03110    1/74   \n",
       "37  KEGG_2018                              Spliceosome  sce03040    1/80   \n",
       "38  KEGG_2018  Protein processing in endoplasmic reticulum  s...    1/89   \n",
       "39  KEGG_2018       Chromosome and associated proteins  sce03036   2/324   \n",
       "40  KEGG_2018                            RNA transport  sce03013    1/94   \n",
       "41  KEGG_2018                               Peptidases  sce01002   1/100   \n",
       "42  KEGG_2018                   Enzymes with EC numbers sce99980   1/104   \n",
       "43  KEGG_2018                              Spliceosome  sce03041   1/107   \n",
       "44  KEGG_2018                         Ubiquitin system  sce04121   1/121   \n",
       "45  KEGG_2018    DNA repair and recombination proteins  sce03400   1/154   \n",
       "46  KEGG_2018                  Transfer RNA biogenesis  sce03016   1/149   \n",
       "47  KEGG_2018                      Ribosome biogenesis  sce03009   1/308   \n",
       "\n",
       "         P-value  Adjusted P-value  Old P-value  Old Adjusted P-value  \\\n",
       "0   8.788324e-07          0.000042     0.001513              0.072619   \n",
       "1   2.662950e-05          0.000639     0.012430              0.198877   \n",
       "2   7.076742e-04          0.008492     0.009147              0.198877   \n",
       "3   5.307089e-04          0.008491     0.117026              0.513072   \n",
       "4   3.451648e-03          0.021854     0.036325              0.253413   \n",
       "5   1.036996e-03          0.009955     0.029072              0.253413   \n",
       "6   3.050112e-03          0.021854     0.032582              0.253413   \n",
       "7   3.876500e-03          0.021854     0.040227              0.253413   \n",
       "8   4.097591e-03          0.021854     0.042235              0.253413   \n",
       "9   2.100805e-02          0.086017     0.075514              0.402742   \n",
       "10  1.483219e-02          0.071195     0.128268              0.513072   \n",
       "11  2.329615e-02          0.086017     0.186124              0.554377   \n",
       "12  3.647933e-02          0.112013     0.122756              0.513072   \n",
       "13  4.665998e-02          0.119311     0.152942              0.554377   \n",
       "14  2.185304e-02          0.086017     0.176717              0.554377   \n",
       "15  3.733755e-02          0.112013     0.269857              0.616816   \n",
       "16  5.673507e-02          0.136164     0.182120              0.554377   \n",
       "17  2.976862e-02          0.102064     0.387649              0.664542   \n",
       "18  4.467252e-02          0.119311     0.308917              0.617834   \n",
       "19  8.390600e-02          0.172675     0.257430              0.616816   \n",
       "20  4.722728e-02          0.119311     0.321884              0.618017   \n",
       "21  6.173337e-02          0.141105     0.196342              0.554377   \n",
       "22  8.633766e-02          0.172675     0.263933              0.616816   \n",
       "23  7.902361e-02          0.172415     0.244256              0.616816   \n",
       "24  1.007952e-01          0.175966     0.301808              0.617834   \n",
       "25  9.359471e-02          0.174255     0.283113              0.617700   \n",
       "26  1.026468e-01          0.175966     0.543849              0.674410   \n",
       "27  9.438788e-02          0.174255     0.516583              0.674410   \n",
       "28  1.428343e-01          0.221163     0.404449              0.669433   \n",
       "29  1.173788e-01          0.191557     0.343612              0.621125   \n",
       "30  1.197233e-01          0.191557     0.349383              0.621125   \n",
       "31  1.586568e-01          0.230773     0.440280              0.674410   \n",
       "32  2.044245e-01          0.254281     0.535686              0.674410   \n",
       "33  1.541655e-01          0.230773     0.430262              0.674410   \n",
       "34  1.741925e-01          0.238106     0.474019              0.674410   \n",
       "35  1.763888e-01          0.238106     0.478675              0.674410   \n",
       "36  1.785794e-01          0.238106     0.483291              0.674410   \n",
       "37  1.916033e-01          0.248566     0.510167              0.674410   \n",
       "38  2.107600e-01          0.254281     0.547958              0.674410   \n",
       "39  2.119009e-01          0.254281     0.789012              0.805799   \n",
       "40  2.212095e-01          0.258977     0.567717              0.681260   \n",
       "41  2.335697e-01          0.266937     0.590324              0.686794   \n",
       "42  2.417028e-01          0.269808     0.604754              0.686794   \n",
       "43  2.477469e-01          0.270269     0.615253              0.686794   \n",
       "44  2.753337e-01          0.293689     0.660778              0.720849   \n",
       "45  3.364923e-01          0.343652     0.748401              0.780940   \n",
       "46  3.275626e-01          0.341804     0.736700              0.780940   \n",
       "47  5.611640e-01          0.561164     0.939946              0.939946   \n",
       "\n",
       "     Z-score  Combined Score                                Genes  \n",
       "0  -1.796607       25.053093  ACT1;YPT7;SSA3;TEF2;PPH21;YOP1;RHO1  \n",
       "1  -1.495411       15.751902      TIM21;ENB1;ITR2;ERV14;VCX1;FET4  \n",
       "2  -1.915461       13.893845                            LAC1;LAG1  \n",
       "3  -1.534199       11.569851       YPT7;SSA3;YIP1;TEF2;RHO1;ERV14  \n",
       "4  -1.848719       10.480207                            CPA1;GFA1  \n",
       "5  -1.371145        9.421723                       YPT7;SSA3;RHO1  \n",
       "6  -1.542810        8.936845                          ALG11;MNN10  \n",
       "7  -1.587780        8.816661                            ACT1;YPT7  \n",
       "8  -1.506383        8.281124                            YPT7;RHO1  \n",
       "9  -1.824594        7.048131                                 CPT1  \n",
       "10 -1.659375        6.987554                            HYP2;TEF2  \n",
       "11 -1.445695        5.435042                           SSA3;PPH21  \n",
       "12 -1.555617        5.150663                                BUD17  \n",
       "13 -1.492773        4.575151                                 ERG1  \n",
       "14 -1.167885        4.465310                           YPT7;PPH21  \n",
       "15 -1.191323        3.916778                            SPA2;RHO1  \n",
       "16 -1.281387        3.676764                                 PUT1  \n",
       "17 -1.000149        3.514824                      ACT1;SSA3;TIM21  \n",
       "18 -1.121502        3.486073                           GRR1;PPH21  \n",
       "19 -1.404926        3.481487                                 SHH3  \n",
       "20 -1.077187        3.288417                           DMC1;PPH21  \n",
       "21 -1.174693        3.271437                                PPH21  \n",
       "22 -1.257325        3.079805                                 GFA1  \n",
       "23 -1.032902        2.621514                                ALG11  \n",
       "24 -1.070079        2.455471                                 CPT1  \n",
       "25 -1.007971        2.387663                                 SSA3  \n",
       "26 -1.048616        2.387135                            ACT1;TEF2  \n",
       "27 -0.822231        1.940748                            ACT1;RGR1  \n",
       "28 -0.879098        1.710787                                 SSA3  \n",
       "29 -0.796126        1.705581                                PPH21  \n",
       "30 -0.773774        1.642392                                 GRR1  \n",
       "31 -0.694820        1.279172                                 ACT1  \n",
       "32 -0.680801        1.080810                                 YPK3  \n",
       "33 -0.577463        1.079698                                ALG11  \n",
       "34 -0.605458        1.058096                                 CPA1  \n",
       "35 -0.581687        1.009264                                 SHH3  \n",
       "36 -0.570718        0.983189                                 SSA3  \n",
       "37 -0.574151        0.948687                                 SSA3  \n",
       "38 -0.562006        0.875064                                 SSA3  \n",
       "39 -0.543684        0.843599                           ACT1;PPH21  \n",
       "40 -0.531269        0.801496                                 TEF2  \n",
       "41 -0.502781        0.731182                                 GFA1  \n",
       "42 -0.366287        0.520145                                 SCS7  \n",
       "43 -0.311198        0.434229                                 SSA3  \n",
       "44 -0.313987        0.404972                                 GRR1  \n",
       "45 -0.155383        0.169240                                 DMC1  \n",
       "46 -0.044488        0.049652                                 TEF2  \n",
       "47  0.413236       -0.238744                                 SSA3  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enr.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/gseapy/plot.py:689: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.\n",
      "  df[self.colname].replace(\n",
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/gseapy/plot.py:689: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[self.colname].replace(\n"
     ]
    }
   ],
   "source": [
    "# to save your figure, make sure that ``ofname`` is not None\n",
    "from gseapy import barplot, dotplot\n",
    "ax = dotplot(enr.results[enr.results['Gene_set']=='KEGG_2018'],column=\"Adjusted P-value\", title='KEGG_2018',cmap='viridis_r',top_term = 20, size=20, figsize=(3,5), cutoff = .1, ofname = '../results/pseudobulk_nonzero/gcn4-top-interactors-kegg.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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